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Deconstructing the most sensationalistic recent findings in Human Brain Imaging, Cognitive Neuroscience, and Psychopharmacology

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    Could one's chronotype (degree of "morningness" vs. "eveningness") be related to your membership on Team white/gold vs. Team blue/black?

    Dreaded by night owls everywhere, Daylight Savings Time forces us to get up an hour earlier. Yes, [my time to blog and] I have been living under a rock, but this evil event and an old tweet by Vaughan Bell piqued my interest in melanopsin and intrinsically photosensitive retinal ganglion cells.


    I thought this was a brilliant idea, perhaps differences in melanopsin genes could contribute to differences in brightness perception. More about that in a moment.


    {Everyone already knows about #thedress from Tumblr and Buzzfeed and Twitter obviously}

    In the initial BuzzFeed poll, 75% saw it as white and gold, rather than the actual colors of blue and black. Facebook's more systematic research estimated this number was only 58% (and influenced by probably exposure to articles that used Photoshop). Facebook also reported differences by sex (males more b/b), age (youngsters more b/b), and interface (more b/b on computer vs. iPhone and Android).

    Dr. Cedar Riener wrote two informativeposts about why people might perceive the colors differently, but Dr. Bell was not satisfied with this and other explanations. Wired consulted two experts in color vision:
    “Our visual system is supposed to throw away information about the illuminant and extract information about the actual reflectance,” says Jay Neitz, a neuroscientist at the University of Washington. “But I’ve studied individual differences in color vision for 30 years, and this is one of the biggest individual differences I’ve ever seen.”
    and
    “What’s happening here is your visual system is looking at this thing, and you’re trying to discount the chromatic bias of the daylight axis,” says Bevil Conway, a neuroscientist who studies color and vision at Wellesley College. “So people either discount the blue side, in which case they end up seeing white and gold, or discount the gold side, in which case they end up with blue and black.”

    Finally, Dr. Conway threw out the chronotype card:
    So when context varies, so will people’s visual perception. “Most people will see the blue on the white background as blue,” Conway says. “But on the black background some might see it as white.” He even speculated, perhaps jokingly, that the white-gold prejudice favors the idea of seeing the dress under strong daylight. “I bet night owls are more likely to see it as blue-black,” Conway says.

    Melanopsin and Intrinsically Photosensitive Retinal Ganglion Cells

    Rods and cones are the primary photoreceptors in the retina that convert light into electrical signals. The role of the third type of photoreceptor is very different. Intrinsically photosensitive retinal ganglion cells (ipRGCs) sense light without vision and:
    • ...contribute to the regulation of pupil size and other behavioral responses to ambient lighting conditions...
    • ...contribute to photic regulation of, and acute photic suppression of, release of the hormone melatonin...

    Recent research suggests that ipRGCs may play more of a role in visual perception than was originally believed. As Vaughan said, melanopsin (the photopigment in ipRGCs) is involved in brightness discrimination and is most sensitive to blue light. Brown et al. (2012) found that melanopsin knockout mice showed a change in spectral sensitivity that affected brightness discrimination; the KO mice needed higher green radiance to perform the task as well as the control mice.

    The figure below shows the spectra of human cone cells most sensitive to Short (S), Medium (M), and Long (L) wavelengths.



    Spectral sensitivities of human cone cells, S, M, and L types. X-axis is in nm.


    The peak spectral sensitivity for melanopsin photoreceptors is in the blue range. How do you isolate the role of melanopsin in humans?  Brown et al. (2012) used metamers, which are...
    ...light stimuli that appear indistinguishable to cones (and therefore have the same color and photopic luminance) despite having different spectral power distributions.  ... to maximize the melanopic excitation achievable with the metamer approach, we aimed to circumvent rod-based responses by working at background light levels sufficiently bright to saturate rods.

    They verified their approach in mice, then used a four LED system to generate stimuli that diffed in presumed melanopsin excitation, but not S, M, or L cone excitation. All six of the human participants perceived greater brightness as melanopsin excitation increased (see Fig. 3E below). Also notice the individual differences in test radiance with the fixed 11% melanopic excitation (on the right of the graph).


    Modified from Fig. 3E (Brown et al. (2012). Across six subjects, there was a strong correlation between the test radiance at equal brightness and the melanopic excitation of the reference stimulus (p < 0.001).1


    Maybe Team white/gold and Team blue/black differ on this dimension? And while we're at it, is variation in melanopsin related to circadian rhythms, chronotype, even seasonal affective disorder (SAD)? 2 There is some evidence in favor of the circadian connections. Variants of the melanopsin (Opn4) gene might be related to chronotype and to SAD, which is much more common in women. Another Opn4 polymorphism may be related to pupillary light responses, which would affect light and dark adaptation. These genetic findings should be interpreted with caution, however, until replicated in larger populations.


    Could This Device Hold the Key to “The Dress”?

    ADDENDUM (March 10 2015):NO, according to Dr. Geoffry K. Aguirre of U. Penn.: Speaking as a guy with a 56-primary version of This Device to study melanopsin, I think the answer to your question is 'no'…” His PNAS paper, Opponent melanopsin and S-cone signals in the human pupillary light response, is freely available.3


    A recent method developed by Cao, Nicandro and Barrionuevo (2015) increases the precision of isolating ipRGC function in humans. The four-primary photostimulator used by Brown et al. (2012) assumed that the rod cells were saturated at the light levels they used. However, Cao et al. (2015) warn that “a four-primary method is not sufficient when rods are functioning together with melanopsin and cones.” So they:
    ...introduced a new LED-based five-primary photostimulating method that can independently control the excitation of melanopsin-containing ipRGC, rod and cone photoreceptors at constant background photoreceptor excitation levels.

    Fig. 2 (Cao et al., 2015). The optical layout and picture of the five-primary photostimulator.


    Their Journal of Vision article is freely available, so you can read all about the methods and experimental results there (i.e., I'm not even going to try to summarize them here).

    So the question remains: beyond the many perceptual influences that everyone has already discussed at length (e.g., color constancy, Bayesian priors, context, chromatic bias, etc.), could variation in ipRGC responses influence how you see “The Dress”?




    Footnotes

    1Fig 3E (continued). The effect was unrelated to any impact of melanopsin on pupil size. Subjects were asked to judge the relative brightness of three metameric stimuli (melanopic contrast −11%, 0%, and +11%) with respect to test stimuli whose spectral composition was invariant (and equivalent to the melanopsin 0% stimulus) but whose radiance changed between trials.

    2This would test Conway's quip that night owls are more likely to see the dress as blue and black.

    3Aguirre also said that a contribution from melanopsin (to the dress effect) was doubtful, at least from any phasic effect: “It's a slow signal with poor spatial resolution and subtle perceptual effects.” It remains to be seen whether any bias towards discarding blue vs. yellow illuminant information is affected by chronotype.

    Interesting result from Spitschan, Jain, Brainard, & Aguirre 2014):
    The opposition of the S cones is revealed in a seemingly paradoxical dilation of the pupil to greater S-cone photon capture. This surprising result is explained by the neurophysiological properties of ipRGCs found in animal studies.

    References

    Brown, T., Tsujimura, S., Allen, A., Wynne, J., Bedford, R., Vickery, G., Vugler, A., & Lucas, R. (2012). Melanopsin-Based Brightness Discrimination in Mice and Humans. Current Biology, 22 (12), 1134-1141 DOI: 10.1016/j.cub.2012.04.039

    Cao, D., Nicandro, N., & Barrionuevo, P. (2015). A five-primary photostimulator suitable for studying intrinsically photosensitive retinal ganglion cell functions in humans. Journal of Vision, 15 (1), 27-27 DOI: 10.1167/15.1.27


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    Website for the BROADEN™ study, which was terminated


    In these days of irrational exuberance about neural circuit models, it's wise to remember the limitations of current deep brain stimulation (DBS) methods to treat psychiatric disorders. If you recall (from Dec. 2013), Neurotech Business Report revealed that "St. Jude Medical failed a futility analysis of its BROADEN trial of DBS for treatment of depression..."

    A recent comment on my old post about the BROADEN Trial1 had an even more pessimistic revelation: there was only a 17.2% chance of a successful study outcome:
    Regarding Anonymous' comment on January 30, 2015 11:01 AM, as follows in part:
    "Second, the information that it failed FDA approval or halted by the FDA is prima facie a blatant lie and demonstratively false. St Jude, the company, withdrew the trial."

    Much of this confusion could be cleared up if the study sponsors practiced more transparency.
    A bit of research reveals that St. Judes' BROADEN study was discontinued after the results of a futility analysis predicted the probability of a successful study outcome to be no greater than 17.2%. (According to a letter from St. Jude)

    Medtronic hasn't fared any better. Like the BROADEN study, Medtronics' VC DBS study was discontinued owing to inefficacy based on futility Analysis.

    If the FDA allowed St. Jude to save face with its shareholders and withdraw the trial rather than have the FDA take official action, that's asserting semantics over substance.

    If you would like to read more about the shortcomings of these major studies, please read (at least):
    Deep Brain Stimulation for Treatment-resistant Depression: Systematic Review of Clinical Outcomes,
    Takashi Morishita & Sarah M. Fayad &
    Masa-aki Higuchi & Kelsey A. Nestor & Kelly D. Foote
    The American Society for Experimental NeuroTherapeutics, Inc. 2014
    Neurotherapeutics
    DOI 10.1007/s13311-014-0282-1

    The Anonymous Commenter kindly linked to a review article (Morishita et al., 2014), which indeed stated:
    A multicenter, prospective, randomized trial of SCC DBS for severe, medically refractory MDD (the BROADEN study), sponsored by St. Jude Medical, was recently discontinued after the results of a futility analysis (designed to test the probability of success of the study after 75 patients reached the 6-month postoperative follow-up) statistically predicted the probability of a successful study outcome to be no greater than 17.2 % (letter from St. Jude Medical Clinical Study Management).

    I (and others) had been looking far and wide for an update on the BROADEN Trial, whether in ClinicalTrials.gov or published by the sponsors. Instead, the authors of an outside review article (who seem to be involved in DBS for movement disorders and not depression) had access to a letter from St. Jude Medical Clinical Studies.

    Another large randomized controlled trial that targeted different brain structures (ventral capsule/ventral striatum, VC/VS) also failed a futility analysis (Morishita et al., 2014):
    Despite the very encouraging outcomes reported in the open-label studies described above, a recent multicenter, prospective, randomized trial of VC/VS DBS for MDD sponsored by Medtronic failed to show significant improvement in the stimulation group compared with a sham stimulation group 16 weeks after implantation of the device. This study was discontinued owing to perceived futility, and while investigators remain hopeful that modifications of inclusion criteria and technique might ultimately result in demonstrable clinical benefit in some cohort of severely debilitated, medically refractory patients with MDD, no studies investigating the efficacy of VC/VS DBS for MDD are currently open.
    In this case, however, the results were published (Dougherty et al., 2014):
    There was no significant difference in response rates between the active (3 of 15 subjects; 20%) and control (2 of 14 subjects; 14.3%) treatment arms and no significant difference between change in Montgomery-Åsberg Depression Rating Scale scores as a continuous measure upon completion of the 16-week controlled phase of the trial. The response rates at 12, 18, and 24 months during the open-label continuation phase were 20%, 26.7%, and 23.3%, respectively.

    Additional studies (with different stimulation parameters, better target localization, more stringent subject selection criteria) are needed, one would say. Self-reported outcomes from the patients themselves range from “...the side effects caused by the device were, at times, worse than the depression itself” to “I feel like I have a second chance at life.”

    So where do we go now?? Here's a tip: all the forward-looking investors are into magnetic nanoparticles these days (see Magnetic 'rust' controls brain activity)...


    Footnote

    1 BROADEN is an tortured acronym for BROdmannArea 25 DEep brain Neuromodulation. The target was subgenual cingulate cortex (aka BA 25). The trial was either halted by the FDA or withdrawn by the sponsor.


    References

    Dougherty DD, Rezai AR, Carpenter LL, Howland RH, Bhati MT, O'Reardon JP, Eskandar EN, Baltuch GH, Machado AD, Kondziolka D, Cusin C, Evans KC, Price LH, Jacobs K, Pandya M, Denko T, Tyrka AR, Brelje T, Deckersbach T, Kubu C, Malone DA Jr. (2014). A Randomized Sham-Controlled Trial of Deep Brain Stimulation of the Ventral Capsule/Ventral Striatum for Chronic Treatment-Resistant Depression. Biol Psychiatry Dec 13. [Epub ahead of print].

    Morishita, T., Fayad, S., Higuchi, M., Nestor, K., & Foote, K. (2014). Deep Brain Stimulation for Treatment-resistant Depression: Systematic Review of Clinical Outcomes. Neurotherapeutics, 11 (3), 475-484. DOI: 10.1007/s13311-014-0282-1



    DBS for MDD targets as of November 2013
    (Image credit: P. HUEY/SCIENCE)

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  • 03/28/15--12:25: Follow #CNS2015


  • Whether or not you're in sunny San Francisco for the start of Cognitive Neuroscience Society Meeting today, you can follow Nick Wan's list of conference attendees on Twitter: @nickwan/#CNS2015. There's also the #CNS2015 hashtag, and the official @CogNeuroNews account.

    Nick will also be blogging from the conference at True Brain. You may see a post or two from The Neurocritic, but I'm usually not very prompt about it. Please comment if you'll be blogging too.

    Two of the program highlights are today:

    Keynote Address, Anjan Chatterjee:
    “The neuroscience of aesthetics and art”

    2015 Distinguished Career Contributions Awardee, Marta Kutas:
    “45 years of Cognitive Electrophysiology: neither just psychology nor just the brain but the visible electrical interface between the twain”


    Here are the CNS interviews with Dr. Chatterjee and Dr. Kutas.

    Enjoy the meeting!

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    What can we do to solve the mind/body problem once and for all? How do we cure devastating brain diseases like Alzheimer's, Parkinson's, schizophrenia, and depression? I am steadfast in following the course of my 500 year plan that may eventually solve these pressing issues, to the benefit of all Americans!

    There's nothing like attending a conference in the midst of a serious family illness to make one take stock of what's important. My mind/brain has been elsewhere lately, along with my body in a different location. My blogging output has declined while I live in this alternate reality. But aside from the disunion caused by depersonalization/derealization, what is my view of the state of Cognitive Neuroscience in 2015?

    But first, let's examine what we're trying to unify. Studies of mind and studies of brain?  Cognition and neuroscience?  Let's start with “neuroscience”.

    Wikipedia says:
    Neuroscience is the scientific study of the nervous system. Traditionally, neuroscience has been seen as a branch of biology. ... The term neurobiology is usually used interchangeably with the term neuroscience, although the former refers specifically to the biology of the nervous system, whereas the latter refers to the entire science of the nervous system. 

    This reminds me of a recent post by Neuroskeptic, who asked: Is Neuroscience Based On Biology? On the face of it, this seemed like an absurd question to me, because the brain is a biological organ and of course we must know its biology to understand how it works. But what he really meant was, Is Cognitive Science Based On Biology? I say this because he adopted a functionalist view and used the brain-as-computer metaphor:
    Could it be that brains are only accidentally made of cells, just as computers are only accidentally made of semiconductors? If so, neuroscience would not be founded on biology but on something else, something analogous to the mathematical logic that underpins computer science. What could this be?

    See John Searle on Beer Cans & Meat Machines (1984):
    This view [the brain is just a digital computer and the mind is just a computer program] has the consequence that there’s nothing essentially biological about the human mind. The brain just happens to be one of an indefinitely large number of different kinds of hardware computers that could sustain the programs which make up human intelligence. ... So, for example, if you made a computer out of old beer cans powered by windmills, if it had the right program. It would have to have a mind.

    The infamous argument-by-beer-cans. In the end, Neuroskeptic admitted he's not sure he subscribes to this view. But the post sparked an interesting discussion. There were a number of good comments, e.g. Jayarava said: “Neuro-science absolutely needs to be neuron-science, to focus on brains made of cells because that's what we need to understand in the first place.” Indeed, some neuroscientists don't consider “cognitive neuroscience” to be “neuroscience” at all, because the measured units are higher (i.e., less reductionist) than single neurons.1

    A comment by Adam Calhoun gets to the heart of the matter, making a sharp point about the disunity of neuroscience:
    Although we use the term 'neuroscience' as though it refers to one coherent discipline, the problem here is that it does not. If you were to pick a neuroscientist at random and ask: "what does your field study?" you will not get the same answer two times in a row.

    Neural development? Molecular pathways? Cognition? Visual processing? Are these the same field? Or different fields that have been given the same name?

    One of the selling points of neuroscience is its interdisciplinary nature, but it's really hard to talk to each other if we don't speak the same language (or work in the same field). Some graduate programs dwell in an idealized world where students can become knowledgeable in molecular, cellular, developmental, systems, and cognitive neuroscience in one year. The reality is that professors in some subfields couldn't pass the exams given in another subfield. And why would they possibly want to do this, given they're way too busy writing grants.

    Sometimes I think cognitive neuroscience is on a completely different planet from the other branches, estranged from even its closest cousin, behavioral neuroscience.2 It's even further away these days from systems neuroscience3 which used to be dominated by the glamour of single unit recordings in monkeys, but now is all about manipulating circuits with opto- and chemogenetics.

    But as the Systems/Circuits techniques get more and more advanced (and invasive and mechanistic), the gulf between animal and human studies grows larger and the prospects for clinical translation fade.  [Until the neuroengineers come in and save the day.]

    I'll end on a more optimistic note, with a quote from a man who wished to bridge the gap between Aplysia californica and Sigmund Freud.





    Footnotes

    1And often not even a direct measure of neural activity at all (e.g. the hemodynamic response in fMRI). The rare exceptions to this are studies in patients with epilepsy, which have revealed the existence of Marilyn Monroe neurons and Halle Berry neurons and (my personal favorite) the rare multimodal Robert Plant neuron in the medial temporal lobe.

    2Though if you look at the mission of the journal called Behavioral Neuroscience, its scope has broadened to include just about anything:
    We seek empirical papers reporting novel results that provide insight into the mechanisms by which nervous systems produce and are affected by behavior. Experimental subjects may include human and non-human animals and may address any phase of the lifespan, from early development to senescence.

    Studies employing brain-imaging techniques in normal and pathological human populations are encouraged, as are studies using non-traditional species (including invertebrates) and employing comparative analyses. Studies using computational approaches to understand behavior and cognition are particularly encouraged.

    In addition to behavior, it is expected that some aspect of nervous system function will be manipulated or observed, ranging across molecular, cellular, neuroanatomical, neuroendocrinological, neuropharmacological, and neurophysiological levels of analysis. Behavioral studies are welcome so long as their implications for our understanding of the nervous system are clearly described in the paper.

    3 Actually, systems neuroscience is mostly about engineering and computational modelling these days.


    Some Final Definitions (for the record)

    The Society for Neuroscience (SfN) explanation of what neuroscientists do:
    Neuroscientists specialize in the study of the brain and the nervous system. They are inspired to try to decipher the brain’s command of all its diverse functions. Over the years, the neuroscience field has made enormous progress. Scientists continue to strive for a deeper understanding of how the brain’s 100 billion nerve cells [NOTE: the number is only 86 billion] are born, grow, and connect. They study how these cells organize themselves into effective, functional circuits that usually remain in working order for life.

    The SfN mission:
    SfN advances the understanding of the brain and the nervous system by bringing together scientists of diverse backgrounds, facilitating the integration of research directed at all levels of biological organization, and encouraging translational research and the application of new scientific knowledge to develop improved disease treatments and cures. 

    The CNS mission:
    The Cognitive Neuroscience Society (CNS) is committed to the development of mind and brain research aimed at investigating the psychological, computational, and neuroscientific bases of cognition.

    The term cognitive neuroscience has now been with us for almost three decades, and identifies an interdisciplinary approach to understanding the nature of thought.

    And according to Wikipedia:
    Cognitive neuroscience is an academic field concerned with the scientific study of biological substrates underlying cognition,[1] with a specific focus on the neural substrates of mental processes. It addresses the questions of how psychological/cognitive functions are produced by neural circuits in the brain. Cognitive neuroscience is a branch of both psychology and neuroscience, overlapping with disciplines such as physiological psychology, cognitive psychology and neuropsychology.[2] Cognitive neuroscience relies upon theories in cognitive science coupled with evidence from neuropsychology and computational modeling.[2]




    Barack Obama, Jan. 24, 2012:
    “…We should all want a smarter, more effective government. And while we may not be able to bridge our biggest philosophical differences this year, we can make real progress. With or without this Congress, I will keep taking actions that help the economy grow. But I can do a whole lot more with your help. Because when we act together, there is nothing the United States of America can’t achieve.”


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    What are the Hot Topics in cognitive neuroscience? We could ask these people, or we could take a more populist approach by looking at conference abstracts. I consulted the program for the recent Cognitive Neuroscience Society meeting (CNS 2015) and made a word cloud using Wordle.1 For comparison, we'll examine the program for the most recent Computational and Systems Neuroscience meeting (Cosyne 2015).

    CNS is all about memory, people, and cognitive processing.

    Cosyne is about neurons, models, and neural activity.




    Word cloud for the 2015 CNS Program



    Word cloud for the 2015 Cosyne Program





    Cosyne is also about network dynamics, information, and learning.




    On the other hand, CNS relies heavily on tasks, studies, and results.








    Both Wordles were constructed from poster titles and abstracts. The Coysne cloud incorporated the titles of talks, and the CNS cloud included titles and abstracts for symposia.1

    What if we only used the abstract titles? Would that provide a more accurate view of the Hot Topics? Since I already had access to a file with the 2014 CNS abstract titles, I started with that.


    Word cloud for CNS 2014 (abstract titles only)


    “Memory” is even more dominant now. “Neural” 2 is brought to the fore, accompanied by “processing” and her younger sibling, “correlates”. Those competitors for memory — “attention”, “language”, “emotional”, et al. — are a wee bit more assertive. {The “visual” bully stays boss of the senses, as usual.} And all those “participants” have faded into the background, relegated to the methods.

    So cognitive neuroscience isn't people after all...

    Finally we have the abstract titles from last year's Organization for Human Brain Mapping meeting3 which, despite the society's name, isn't about people either (although humans and their diseases do play a dramatic role). Instead, “connectivity” is king!


    Word cloud for OHBM 2014 (abstract titles only)


    In a real stunner for a methods-driven conference on human brain mapping, “ brain” and “fmri” were key terms, and functional connectivity the key concept.


    These Wordles were inspired by the tweets of ‏@CousinAmygdala, who made some lovely word clouds after the first NIH BRAIN Initiative Awards were announced (see also Neuroecology).





    Not CNS-friendly, I'm afraid...




    Footnotes

    1 For the Wordle word clouds, I didn't include ads or indices, and edited out common affiliation words like "University". I set all words to appear in lower case to collapse occurrences of words like "Neural" and "neural".

    2 Although what counts as “neural” here differs (for the most part) from what the word means elsewhere (e.g., the BOLD signal vs. direct physiological recordings from neurons).

    3 This required a bit of editing to extract the poster titles. The final document wasn't as pristine as the 2014 CNS text, but major place names (i.e., affiliations) were removed.


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    The U.S. Food and Drug Administration recently admonished TauMark™, a brain diagnostics company, for advertising brain scans that can diagnose chronic traumatic encephalopathy (CTE), Alzheimer's disease, and other types of dementia. The Los Angeles Times reported that the FDA ordered UCLA researcher Dr. Gary Small and his colleague/business partner Dr. Jorge Barrio to remove misleading information from their company website (example shown below).




    CTE has been in the news because the neurodegenerative condition has been linked to a rash of suicides in retired NFL players, based on post-mortem observations. And the TauMark™ group made headlines two years ago with a preliminary study claiming that CTE pathology is detectable in living players (Small et al., 2013).

    The FDA letter stated:
    The website suggests in a promotional context that FDDNP, an investigational new drug, is safe and effective for the purpose for which it is being investigated or otherwise promotes the drug. As a result, FDDNP is misbranded under section 502(f)(1) of the FD&C Act...

    [18F]-FDDNP1 is a molecular imaging probe that crosses the blood brain barrier and binds to several kinds of abnormal proteins in the brain. When tagged with a radioactive tracer, FDDNP can be visualized using PET (positron emission tomography).

    Despite what the name of the company implies, FDDNP is not an exclusive tau marker. FDDNP may bind to tau protein[although this is disputed],2 but it also binds to beta-amyloid, found in the clumpy plaques that form in the brains of those with Alzheimer's disease. Tau is found in neurofibrillary tangles, also characteristic of Alzheimer's pathology, and seen in other neurodegenerative tauopathies such as CTE.

    The big deal with this and other radiotracers is that the pathological proteins can now be visualized in living human beings. Previously, an Alzheimer's diagnosis could only be given at autopsy, when the post-mortem brain tissue was processed to reveal plaques and tangles. So PET imaging is a BIG improvement. But still, a scan alone is not completely diagnostic, as noted by the Alzheimer's Association:
    Even though amyloid plaques in the brain are a characteristic feature of Alzheimer's disease, their presence cannot be used to diagnose the disease. Many people have amyloid plaques in the brain but have no symptoms of cognitive decline or Alzheimer's disease. Because amyloid plaques cannot be used to diagnose Alzheimer's disease, amyloid imaging is not recommended for routine use in patients suspected of having Alzheimer's disease.

    from TauMark's old website


    There are currently three FDA-approved molecular tracers that bind to beta-amyloid: florbetapirflutemetamol, and florbetaben (note that none of these is FDDNP). But the big selling point of TauMark™ is (of course) the tau marker part, which would also label tau in the brains of individuals with CTE and frontotemporal dementia, diseases not characterized by amyloid plaques. But how can you tell the difference, when FDDNP targets plaques and tangles (and prion proteins, for that matter)?

    A new study by the UCLA team demonstrated that the distribution of FDDNP labeling in the brains of Alzheimer's patients differs from that seen in a selected group of former NFL players with cognitive complaints (Barrio et al., 2015). These retired athletes (and others with a history of multiple concussions) are at risk of developing the brain pathology known as chronic traumatic encephalopathy.



    from Fig. 1 (Barrio et al., 2015).  mTBI = mild traumatic brain injury, or concussion. T1 to T4 = progressive FDDNP PET signal patterns.


    It's a well-established fact that brains with Alzheimer's disease, frontotemporal lobar degeneration, or Lou Gehrig's disease (for example) all show different patterns of neurodegeneration, so why not extend this to CTE? This may seem like a reasonable approach, but there are problems with some of the assumptions.




    Perhaps the most deceptive claim is that “TauMark owns the exclusive license of the first and only brain measure of tau protein...” Au contraire! A review of recent developments in tau PET imaging (Zimmer et al., 2014) said that...
    ...six novel tau imaging agents—[18F]THK523, [18F]THK5105, [18F]THK5117, [18F]T807, [18F]T808, and [11C]PBB3—have been described and are considered promising as potential tau radioligands.

    Note that [18F]FDDNP is not among the six.2,3  In fact, Zimmer et al. (2014) mentioned that in brain slices, “[3H]FDDNP failed to demonstrate overt labeling of tau pathology.” 2

    No matter. Former NFL players are clamoring to participate in the TauMark studies.


    So to recap, the FDA considered TauMark marketing to be “concerning from a public health perspective.” Their letter warned:
    Your website describes FDDNP for use in brain PET scans to diagnose traumatic brain injuries, Alzheimer’s disease, and other neurological conditions. These uses are ones for which a prescription would be needed because they require the supervision of a physician and adequate directions for lay use cannot be written.
    (see also Regulatory Focus News and the FDA's own PDF archive of the TauMark site).


    At this point, astute followers of The Neurocritic and Neurobollocks might ask, “Hey, how does Dr. Daniel Amen get away with claiming that his SPECT scans can accurately diagnose different types of dementia, each with different ‘treatment plans’?”




    Hey FDA, what gives?? Dr. Small and Dr. Barrio have at least 37 peer-reviewed publications on their FDDNP methods and imaging results. Meanwhile, Dr. Amen has two non-peer reviewed poster abstracts on his SPECT results in dementia. With ads like these and appearances on Celebrity Rehab, aren't the Amen Clinics's claims “misbranded” too?



    Further Reading

    Is CTE Detectable in Living NFL Players?

    The Ethics of Public Diagnosis Using an Unvalidated Method

    Uncertain Diagnoses, Research Data Privacy, & Preference Heterogeneity

    Blast Wave Injury and Chronic Traumatic Encephalopathy: What's the Connection?

    Little Evidence for a Direct Link between PTSD and Chronic Traumatic Encephalopathy


    Footnotes

    1FDDNP is 2-(1-(6-[(2-[(18)F]fluoroethyl)(methyl)amino]-2-naphthyl)ethylidene)malononitrile.

    2 Or what is presumed to be tau. FDDNP is supposedly a tracer for both tau and amyloid, but some experts think it's neither. Zimmer et al. (2014) stated:
    Though ... [18F]FDDNP appeared to bind both amyloid plaques and tau tangles, a subsequent study using [3H]FDDNP autoradiography in sections containing neurofibrillary tangles (NFTs) failed to demonstrate overt labeling of tau pathology because of a low affinity for NFTs.
    Other studies have shown that it binds to a variety of misfolded proteins.

    3James et al. (2015) were more generous in their review of tau PET imaging, mentioning the existence of seven tau tracers (including FDDNP). But again they noted the lack of specificity.  (Parenthetically speaking, [18F]T807 imaging has been done in a single NFL player, which may be of interest in a future post.)


    References

    Barrio, J., Small, G., Wong, K., Huang, S., Liu, J., Merrill, D., Giza, C., Fitzsimmons, R., Omalu, B., Bailes, J., & Kepe, V. (2015). In vivo characterization of chronic traumatic encephalopathy using [F-18]FDDNP PET brain imaging. Proceedings of the National Academy of Sciences DOI: 10.1073/pnas.1409952112

    Zimmer, E., Leuzy, A., Gauthier, S., & Rosa-Neto, P. (2014). Developments in Tau PET Imaging. The Canadian Journal of Neurological Sciences, 41 (05), 547-553 DOI: 10.1017/cjn.2014.15

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    A new study has found that the pain reliever TYLENOL® (acetaminophen) not only dampens negative emotions, it blunts positive emotions too. Or does it?

    Durso and colleagues (2015) reckoned that if acetaminophen can lessen the sting of psychological pain (Dewall et al., 2010; Randles et al., 2013) — which is doubtful in my view then it might also lessen reactivity to positive stimuli. Evidence in favor of their hypothesis would support differential susceptibility, the notion that the same factors govern reactivity to positive and negative experiences.1 This outcome would also contradict the framework of acetaminophen as an all-purpose treatment for physical and psychological pain.

    The Neurocritic is not keen on TYLENOL® as a remedy for existential dread or social rejection. In high doses acetaminophen isn't great for your liver, either. And a recent meta-analysis even showed that it's ineffective in treating lower back pain (Machado et al., 2015)...

    But I'll try to be less negative than usual. The evidence presented in the main manuscript supported the authors' hypothesis. Participants who took acetaminophen rated positive and negative IAPS pictures as less emotionally arousing compared to a separate group of participants on placebo. The drug group also rated the unpleasant pictures less negatively and the pleasant pictures less positively. “In all, rather than being labeled as merely a pain reliever, acetaminophen might be better described as an all-purpose emotion reliever,” they concluded (Durso et al., 2015).

    Appearing in the prestigious Journal of Psychological Acetaminophen Studies, the paper described two experiments on healthy undergraduates, both of which yielded a raft of null results.

    Wait a minute..... what? How can that be?

    The main manuscript reported the results collapsed across the two studies, and the Supplemental Material presented the results from each experiment separately. Why does this matter?
    Eighty-two participants in Study 1 and 85 participants in Study 2 were recruited to participate in an experiment on “Tylenol and social cognition” in exchange for course credit. Our stopping rule of at least 80 participants per study was based on previously published research on acetaminophen (DeWall et al., 2010; Randles et al., 2013), in which 30 to 50 participants were recruited per condition (i.e., acetaminophen vs. a placebo).  ... The analyses reported here for the combined studies are reported for each study separately in the Supplemental Material available online.

    What this means is that the authors violated their stopping rule, and recruited twice the number of participants as originally planned. Like the other JPAS articles, this was a between-subjects design (unfortunately), and there were over 80 participants in each condition (instead of 30 to 50).

    After running Experiment 1, the authors were faced with results like these:
    As expected, however, a main effect of treatment (though not significantly significant in this study) was obtained, F(1,72) = 2.15, p = .147, ηp2 = .029, as was the predicted interaction (although it was not statistically significant in this study), F(3.3, 240.3) = 1.15, p = .330, ηp2 = .016. Contrast analyses indicated that participants taking acetaminophen were marginally significantly less emotionally aroused by extremely pleasant stimuli (M = 5.01, SD = 1.75) than were participants taking placebo (M = 5.65, SD = 1.55), t(72) = 1.67,p = .099. Similarly, participants receiving acetaminophen were less emotionally aroused by extremely unpleasant stimuli (M = 6.88, SD = 1.25) than were participants assigned the placebo condition (M = 7.23, SD = 1.84), although this difference was not statistically significant in this study, t(72) = 0.96, p = .341. Furthermore, participants taking acetaminophen tended to be less emotionally aroused by moderately pleasant stimuli (M = 2.91, SD = 1.64) than participants taking placebo (M = 3.49, SD = 1.89), t(72) = 1.44, p = .155, and participants taking acetaminophen also tended to be less emotionally aroused by moderated unpleasant stimuli (M = 4.68, SD = 1.42) than participants taking placebo (M = 5.25, SD = 2.02), t(72) = 1.42, p = .161, although these differences were not statistically significant in this study. 

    Wow, what a disappointment to get these results. Nothing looks statistically significant!

    Let's look at Experiment 2:
    ...Contrast analyses revealed that participants taking acetaminophen tended to rate extremely unpleasant stimuli (M = -3.39, SD = 1.14) less negatively than participants receiving placebo (M = -3.74, SD = 0.74), t(77) = 1.60, p = .115, though this contrast was not itself statistically significant within this study. Participants taking acetaminophen also rated extremely pleasant stimuli (M = +2.51, SD = 1.07) significantly less positively than participants receiving placebo (M = +3.19, SD = 0.88), t(77) = 3.06, p = .003.

    Participants taking acetaminophen also tended to evaluate moderately pleasant stimuli (M = +1.15, SD = 0.91) less positively than participants receiving placebo (M = +1.42, SD = 0.89), t(77) = 1.30, p = .198, although this difference was not statistically significant in this study. Finally, participants taking acetaminophen tended to rate moderately unpleasant stimuli less negatively (M = -1.84, SD = 0.99) than participants taking placebo (M = -1.93, SD = 0.95), although this difference wasnot significantin this study, t(77) = 0.42, p = .678. [NOTE:"tended"? really?] Evaluations of neutral stimuli surprisingly differed as a function of treatment, t(77) = 2.94, p = .004, such that participants taking acetaminophen evaluated these stimuli significantly less positively (M = -0.05, SD = 0.42) than did participants taking placebo (M = +0.22, SD = 0.38).

    One of the arguments that acetaminophen affects ratings of emotional stimuli specifically (both positive and negative) is that it does not affect ratings for neutral stimuli. Yet it did here. So in the paragraphs above, extremely pleasant stimuli and neutral stimuli were both rated as less positive by the drug group, but ratings for extremely unpleasant, moderately pleasant, and moderately unpleasant pictures did not differ between drug and placebo groups.

    The subjective emotional arousal ratings fared better than the picture ratings in Experiment 2, but there were still some unexpected and non-significant results. Overall, support for the “acetaminophen as an all-purpose emotion reliever” was underwhelming when the studies are examined singly (which is how they were run). 2

    [Right about now you're saying, “Hey! I thought you said you'd be less negative here!”]

    Let's accept that the combined results reported in the main manuscript present a challenge to the “acetaminophen as a psychological pain reliever” view, and support the differential susceptibility hypothesis. To convince those of us outside the field of social psychology, it would be beneficial to: (1) design within-subjects experiments, and (2) seriously consider possible mechanisms of action, beyond speculations about serotonin and (gasp!) 5-HTTLPR. For instance, why choose acetaminophen (which may act via the spinal cord) and not aspirin or ibuprofen? 3

    At the risk of sounding overbearing and pedantic, I hereby issue the following friendly suggestions to all TYLENOL® psychology researchers...


    The Proper Pharmacological Study Design Challenge

    (1) Please consider using a double-blind, randomized crossover design, like studies that have examined IAPS picture ratings after acute administration of SSRI antidepressants or placebo in healthy participants (Kemp et al., 2004; van der Veen et al., 2012; Outhred et al., 2014).

    Speaking of SSRIs, did you know that citalopram did not alter IAPS valence or arousal ratings relative to placebo (Kemp et al., 2004)? Or that paroxetine produced only minor effects on valence and arousal ratings for two of the eight conditions (van der Veen et al., 2012)? 4 What are the implications of these findings for your theoretical framework, that an OTC pain reliever supposedly has a greater impact on emotional processing than a prescription antidepressant? And that before the recent JPAS papers, no one has ever suspected that TYLENOL® affects reactions to emotionally evocative stimuli or David Lynch films?

    (2) Please consider that acetaminophen may act via COX-1, COX-2, COX-3, peroxidase, nitric oxide synthase, cannabinoid receptors, and/or descending serotoninergic projections to the spinal cord (Toussaint et al., 2010) before mentioning the anterior cingulate cortex or the serotonin transporter gene. Just another friendly suggestion.

    I usually give all my ideas away for free, but if you're interested in hiring me as a consultant, please leave a comment.


    ADDENDUM (May 6 2015): A commentbyDr. R(who developed theReplication-Index) said there was nothing wrong with combining the two studies. Study 1 was non-significant but Study 2 was significant, and combined the results were statistically credible (although I'm not exactly sure which of the many tests he checked). Perhaps one source of trouble was that Durso et al.'s estimated number of participants was based on inflated effect sizes in the earlier papers...


    Footnotes

    1 Turns out differential susceptibility is more or less The Orchid and the Dandelion, or as author David Dobbs puts it, “some of the genes and traits generating our greatest maladies and misdeeds — depression, anxiety, hyper-aggression, a failure to focus — also underlie many of our greatest satisfactions and success." I don't really see how this acetaminophen study informs the differential susceptibility hypothesis, which is based on individual differences (beyond a metaphorical kinship, perhaps).

    2 But then I missed the memo from Psych Sci on “recently recommended approaches to presenting the results of multiple studies through combined analyses.”  [paging @mc_hankins...]

    3 I know the original social rejection study used Tylenol, but why does everyone persist in doing so?? I was pleased to see that in the press release, first author Geoffrey Durso said they're branching out to test ibuprofen and aspirin.  [There, something positive.]

    4 To be precise, participants gave lower arousal ratings to high arousal, low valence pictures and slightly lower valence ratings to high arousal, high valence pictures. The other six cells in the arousal/pleasure ratings of high/low arousal, high/low pleasure were no different on drug vs. placebo.


    References

    Dewall CN, Macdonald G, Webster GD, Masten CL, Baumeister RF, Powell C, Combs D, Schurtz DR, Stillman TF, Tice DM, Eisenberger NI. (2010). Acetaminophen reduces social pain: behavioral and neural evidence. Psychol Sci. 21:931-7.

    Durso, G., Luttrell, A., & Way, B. (2015). Over-the-Counter Relief From Pains and Pleasures Alike: Acetaminophen Blunts Evaluation Sensitivity to Both Negative and Positive Stimuli. Psychological Science DOI: 10.1177/0956797615570366

    Kemp AH, Gray MA, Silberstein RB, Armstrong SM, Nathan PJ. (2004). Augmentation of serotonin enhances pleasant and suppresses unpleasant cortical electrophysiological responses to visual emotional stimuli in humans. Neuroimage 22:1084-96.

    Machado GC, Maher CG, Ferreira PH, Pinheiro MB, Lin CW, Day RO, McLachlan AJ, Ferreira ML. (2015). Efficacy and safety of paracetamol for spinal pain and osteoarthritis: systematic review and meta-analysis of randomised placebo controlled trials. BMJ. 350:h1225.

    Outhred T, Das P, Felmingham KL, Bryant RA, Nathan PJ, Malhi GS, Kemp AH. (2014). Impact of acute administration of escitalopram on the processing of emotional and neutral images: a randomized crossover fMRI study of healthy women. J Psychiatry Neurosci. 39:267-75.

    Randles D, Heine SJ, Santos N. (2015). The common pain of surrealism and death: acetaminophen reduces compensatory affirmation following meaning threats. Psychol Sci. 24:966-73.

    van der Veen FM, Jorritsma J, Krijger C, Vingerhoets AJ. (2012). Paroxetine reduces crying in young women watching emotional movies. Psychopharmacology 220:303-8.


    Better get this woman a damn fine cup of coffee and 1000 mg of TYLENOL®




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    I have two heads
    Where's the man, he's late

    --Throwing Muses, Devil's Roof


    Medical journals are enlivened by case reports of bizarre and unusual syndromes. Although somaticdelusions are relatively common in schizophrenia, reports of hallucinations and delusions of bicephaly are rare. For a patient to attempt to remove a perceived second head by shooting and to survive the experience for more than two years may well be unique, and merits presentation.

    --David Ames, British Journal of Psychiatry (1984)

    In 1984, Dr. David Ames of Royal Melbourne Hospital published a truly bizarre case report about a 39 year old man hospitalized with a self-inflicted gunshot wound through the left frontal lobe (Ames, 1984). The man was driven to this desperate act by the delusion of having a second head on his shoulder. The interloping head belonged to his wife's gynecologist.




    In an even more macabre twist, his wife had died in a car accident two years earlier..... and the poor man had been driving at the time!

    Surprisingly, the man survived a bullet through his skull (in true Phineas Gage fashion). After waking from surgery to remove the bullet fragments, the patient was interviewed:
    He described a second head on his shoulder. He believed that the head belonged to his wife's gynaecologist, and described previously having felt that his wife was having an affair with this gynaecologist, prior to her death. He described being able to see the second head when he went to bed at night, and stated that it had been trying to dominate his normal head. He also stated that he was hearing voices, including the voice of his wife's gynaecologist from the second head, as well as the voices of Jesus and Abraham around him, conversing with each other. All the voices were confirming that he had two heads...

    I'm two headed one free one sticky
    --Throwing Muses, Devil's Roof

    The other head kept trying to dominate my normal head, and I would not let it. It kept trying to say to me I would lose, and I said bull-shit ... and decided to shoot my other head off.”

    A gun was not his first choice, however... he originally wanted to use an ax.




    He stated that he fired six shots, the first at the second head, which he then decided was hanging by a thread, and then another one through the roof of his mouth. He then fired four more shots, one of which appeared to have gone through the roof of his mouth and three of which missed. He said that he felt good at that stage, and that the other head was not felt any more. Then he passed out. Prior to shooting himself, he had considered using an axe to remove the phantom head.

    Not surprisingly, the patient was diagnosed with schizophrenia and given antipsychotics.
    He was seen regularly in psychiatric out-patients following this operation and by March, stated that the second head was dead, that he was taking his chlorpromazine regularly, and that he had no worries.  [This was Australia, after all.]

    Unfortunately, the man died two years later from a Streptococcus pneumoniae infection in his brain.  Ames (1984) concluded his lively and bizarre case report by naming the singular syndromeperceptual delusional bicephaly”:
    This case illustrates an interesting phenomenon of perceptual delusional bicephaly; the delusion caused the patient to attempt to remove the second head by shooting. It is notable that following his head injury and treatment with chlorpromazine, the initial symptoms resolved, although he was left with the problems of social disinhibition and poor volition, typical of patients with frontal lobe injuries.

    As far as I know, this specific delusion has not yet been depicted in a horror film (or in an episode of Perception or Black Box).


    Reference

    Ames, D. (1984). Self shooting of a phantom head The British Journal of Psychiatry, 145 (2), 193-194 DOI: 10.1192/bjp.145.2.193





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    Capgras syndrome is the delusion that a familiar person has been replaced by a nearly identical duplicate. The imposter is usually a loved one or a person otherwise close to the patient.

    Originally thought to be a manifestation of schizophrenia and other psychotic illnesses, the syndrome is most often seen in individuals with dementia (Josephs, 2007). It can also result from acquired damage to a secondary (dorsal) face recognition system important for connecting the received images with an affective tone (Ellis & Young, 1990).1 Because of this, the delusion crosses the border between psychiatry and neurology.

    The porous etiology of Capgras syndrome raises the question of how phenomenologically similar delusional belief systems can be constructed from such different underlying neural malfunctions. This is not a problem for Freudian types, who promote psychodynamic explanations (e.g., psychic conflict, regression, etc.). For example, Koritar and Steiner (1988) maintain that “Capgras' Syndrome represents a nonspecific symptom of regression to an early developmental stage characterized by archaic modes of thought, resulting from a relative activation of primitive brain centres.”

    The psychodynamic view was nicely dismissed by de Pauw (1994), who states:
    While often ill-founded and convoluted, these formulations have, until recently, dominated many theoretical approaches to the phenomenon. Generally post hoc and teleological in nature, they postulate motives that are not introspectable and defence mechanisms that cannot be observed, measured or refuted. While psychosocial factors can and often do play a part in the development, content and course of the Capgras delusion in individual patients it remains to be proven that such factors are necessary and sufficient to account for delusional misidentification in general and the Capgras delusion in particular.

    Canary Capgras

    Although psychodynamic explanations were sometimes applied 2 to cases of Capgras syndrome for animals,3 other clinicians report that the delusional misindentification of pets can be ameliorated by pharmacological treatment of the underlying psychotic disorder. Rösler et al. (2001) presented the case of “a socially isolated woman who felt her canary was replaced by a duplicate”:
    Mrs. G., a 67-year-old woman, was admitted for the first time to a psychiatric hospital for late paraphrenia. ... She had been a widow for 11 years, had no children, and lived on her own with very few social contacts. Furthermore, she suffered from concerns that her canary was alone at home. She was delighted with the suggestion that the bird be transferred to the ward. However, during the first two days she repeatedly asserted that the canary in the cage was not her canary and reported that the bird looked exactly like her canary, but was in fact a duplicate. There were otherwise no misidentifications of persons or objects.

    Earlier, Somerfield (1999) had reported a case of parrot Capgras, also in an elderly woman with a late-onset delusional disorder:
    I would like to report an unusual case of a 91-year-old woman with a 10-year history of late paraphrenia (LP) and episodes of Capgras syndrome involving her parrot. She was a widow of 22 years, nulliparous, with profound deafness and a fiercely independent character.  The psychotic symptoms were usually well controlled by haloperidol 0.5 mg orally. However, she was periodically non-compliant with medication, resulting in deterioration of her mental state, refusal of food and her barricading herself in her room to stop her parrot being stolen. At times she accused others of “swapping” the parrot and said the bird was an identical imposter. There was no misidentifcation of people or objects. Her symptoms would attenuate rapidly with reinstatement of haloperidol.

    Both of these patients believed their beloved pet birds had been replaced by impostors, but neither of them misidentified any human beings. Clearly, this form of Capgras syndrome is different from what can happen after acquired damage to the affective face identification system (Ellis & Young, 1990). Is there an isolated case of sudden onset Capgras for animals that does not encompass person identification as well? I couldn't find one.


    A Common Explanation?

    Despite these differences, Ellis and Lewis (2001) suggested that “It seems parsimonious to seek a common explanation for the delusion, regardless of its aetiology.” I'm not so sure. If that's true, then haloperidol should effectively treat all instances of Capgras syndrome, including those that arise after a stroke. And there's evidence suggesting that antipsychotics would be ineffective in such patients.

    Are there systematic differences in the symptoms shown by Capgras patients with varying etiologies? Josephs (2007) reviewed 47 patient records and found no major differences between the delusions in patients with neurodegenerative vs. non-neurodegenerative disorders. In all 47 cases, the delusion involved a spouse, child, or other relative. {There were no cases involving animals or objects.}



    The factors that did differ were age of onset (older in dementia patients) and other reported symptoms (e.g., visual hallucinations 4 in all patients with Lewy body dementia, LBD). In this series, 81% of patients had a neurodegenerative disease, and only 4% had schizophrenia [perhaps the Capgras delusion was under-reported in the context of wide-ranging delusions?]. Other cases were due to methamphetamine abuse (4%) or sudden onset brain injury, e.g. hemorrhage (11%).

    Interestingly, Josephs puts forth dopamine dysfunction as a unifying theme, in line with Ellis and Lewis's general suggestion of a common explanation. The pathology in dementia with Lewy bodies includes degeneration of neurons containing dopamine and acetylcholine. The cognitive/behavioral symptoms of LBD overlap with those seen in Parkinson's dementia, which also involves degeneration of dopaminergic neurons. But dopamine-blocking antipsychotics like haloperidol should not be used in treating LBD. So from a circuit perspective, using “dopamine dysregulation” as a parsimonious explanation isn't really an explanation. And this conception doesn't fit with the neuropsychological model (shown at the bottom of the page).

    I'm not a fan of parsimony in matters of brain function and dysfunction. We don't know why one person thinks her canary has been replaced by an impostor, another thinks her husband has been replaced by a woman, while a third is convinced there are six copies of his wife floating around.5 I don't expect there to be a unifying explanation. The BRAIN Initiative and the Human Brain Project will teach us absolutely nothing about the content of delusions. Ultimately, the study of Capgras and other delusional misidentification syndromes present a challenging puzzle for those of us seeking neural explanations of thought and behavior.


    Footnotes

    1From Ellis and Young (1990). Also see figure below.
    Bauer (1984, 1986) advanced the view that there are two routes to facial recognition. The main route runs from visual cortex to temporal lobes via the inferior longitudinal fasciculus....the 'vental route' corresponds to the system responsible for overt or conscious recognition, and it is the route which typically is damaged in cases of prosopagnosia. The other, described as the 'dorsal route', runs between the visual cortex and the limbic system, via the inferior parietal lobule, and is sometimes intact in prosopagnosic patients. It is this latter route which ... gives the face its emotional significance and hence, when the ventral route is selectively damaged, can give rise to covert recognition (i.e. recognition at an unconscious level).

    2Canine Capgras:
    Reports 2 separate cases (a 76-yr-old woman and a 57-yr-old woman) in which the S believed that her pet dog had been replaced by an identical double. The psychodynamic issues that these cases raise are discussed. [NOTE: I don't have access to this article, sorry I can't say more.] In the Capgras delusion the double is usually a key figure in the life of the patient.

    3 Capgras for animals was dubbed zoocentric Capgras syndrome by Ehrt (1999). He presented the “case of a 23-year old women who had the delusional belief that her cat had been replaced by the cat of her former boy-friend.”

    4 There are a number of interesting hypotheses on why visual hallucinations are so common in Lewy body dementias.

    5 Unless he's a character in Orphan Black... But really, why six copies instead of three? What I mean here is an explanation beyond the trivial: one person lives alone with a canary, while the other two live with a spouse.


    References

    de Pauw KW. (1994). Psychodynamic approaches to the Capgras delusion: a critical historical review. Psychopathology 27(3-5):154-60.

    Ellis HD, Lewis MB. (2001). Capgras delusion: a window on face recognition. Trends Cogn Sci. 5(4):149-156.

    Ellis, H., & Young, A. (1990). Accounting for delusional misidentifications. The British Journal of Psychiatry, 157 (2), 239-248 DOI: 10.1192/bjp.157.2.239

    Josephs, K. (2007). Capgras Syndrome and Its Relationship to Neurodegenerative Disease. Archives of Neurology, 64 (12) DOI: 10.1001/archneur.64.12.1762

    Koritar E, Steiner W. (1988). Capgras' syndrome: a synthesis of various viewpoints. Can J Psychiatry 33(1):62-6.

    Rösler, A., Holder, G., & Seifritz, E. (2001). Canary Capgras. The Journal of Neuropsychiatry and Clinical Neurosciences, 13 (3), 429-429 DOI: 10.1176/jnp.13.3.429

    Somerfield D. (1999). Capgras syndrome and animals. Int J Geriatr Psychiatry 14(10):893-4.




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    Scene from Elephant, a fictional film by Gus Van Sant


    Regular use of over-the-counter pain relievers like aspirin, ibuprofen, naproxen, and acetaminophen was associated with three times the risk of committing a homicide in a new Finnish study (Tiihonen et al., 2015). The association between NSAID use and murderous acts was far greater than the risk posed by antidepressants.

    Clearly, drug companies are pushing dangerous, toxic chemicals and we should ban the substances that are causing school massacres Advil and Alleve and Tylenol are evil!!

    Wait..... what?


    Tiihonen and colleagues wanted to test the hypothesis that antidepressant treatment is associated with an increased risk of committing a homicide. Because, you know, the Scientology-backed Citizens Commission on Human Rights of Colorado thinks so (and their blog is cited in the paper!!):
    After a high-profile homicide case, there is often discussion in the media on whether or not the killing was caused or facilitated by a psychotropic medication. Antidepressants have especially been blamed by non-scientific organizations for a large number of senseless acts of violence, e.g., 13 school shootings in the last decade in the U.S. and Finland [1].

    The authors reviewed a database of all homicides investigated by the police in Finland between 2003 and 2011. A total of 959 offenders were included in the analysis. Each offender was matched to 10 controls selected from the Population Information System. Then the authors checked purchases in the Finnish Prescription Register. A participant was considered a "user" if they had a current purchase in the system.1

    The main drug classes examined were antidepressants, benzodiazepines, and antipsychotics. The primary outcome measure was risk of offending for current use vs. no use of those drugs (with significance set to p<0.016 to correct for multiple comparisons). Seven other drug classes were examined as secondary outcome measures (with α adjusted to .005): opioid analgesics, non-opioid analgesics (e.g., NSAIDs), antiepileptics, lithium, stimulants, meds for addictive disorders, and non-benzo anxiolytics.

    Lo and behold, current use of antidepressants in the adult offender population was associated with a 31% greater risk of committing a homicide, but this did not reach significance (p=0.022). On the other hand, benzodiazepine use was associated with a 45% greater risk (p<.001), while antipsychotics were not associated with greater risk of offending (p=0.54).

    Most dangerous of all were pain relievers. Current use of opioid analgesics (like Oxycontin and Vicodin) was associated with 92% greater risk. Non-opioid analgesics were even worse: individuals taking these meds were at 206% greater risk of offending that's a threefold increase.2  Taken in the context of this surprising result, the anti-psych-med faction doth complain too much about antidepressants.

    Furthermore, analysis of young offenders (25 yrs or less) revealed that none of the medications were associated with greater risk of committing a homicide (benzos and opioids were p=.07 and .04 respectively). To repeat: In Finland at least, there was no association between antidepressant use and the risk of becoming a school shooter.

    What are we to make of the provocative NSAIDs? More study is needed:
    The surprisingly high risk associated with opioid and non-opioid analgesics deserves further attention in the treatment of pain among individuals with criminal history.

    Drug-related murders in oxycodone abusers don't come as a great surprise, but aspirin-related violence is hard to explain...3


    Footnotes

    1 Having a purchase doesn't mean the individual was actually taking the drug before/during the time of the offense, however.

    2 RR = 3.06; 95% CI: 1.78-5.24, p<0.001 for Advil, Tylenol, and the like. And the population-adjusted odds ratios (OR) weren't substantially different, although this wasn't reported for NSAIDs:
    The analysis based on case-control design showed an adjusted OR of 1.30 (95% CI: 0.97-1.75) as the risk of homicide for the current use of an antidepressant, 2.52 (95% CI: 1.90-3.35) for benzodiazepines, 0.62 (95% CI: 0.41-0.93) for antipsychotics, and 2.16 (95% CI: 1.41-3.30) for opioid analgesics.

    3 P.S. Just to be clear here, correlation ≠ causation. Disregarding the anomalous nature of the finding in the first place, it could be that murderers have more headaches and muscle pain, so they take more anti-inflammatories (rather than ibuprofen "causing" violence). But if the anti-med faction uses these results to argue that "antidepressants cause school shootings" then explain how ibuprofen raises the risk threefold...


    Reference

    Tiihonen, J., Lehti, M., Aaltonen, M., Kivivuori, J., Kautiainen, H., J. Virta, L., Hoti, F., Tanskanen, A., & Korhonen, P. (2015). Psychotropic drugs and homicide: A prospective cohort study from Finland. World Psychiatry, 14 (2), 245-247. DOI: 10.1002/wps.20220

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    Fellini/Schultz: 8½ Reward Prediction Errors



    On Twitter, movie/brain buff My Cousin Amygdala issued the #MovieDirectorNeuroscientistMashup challenge using the following selections:


    I made a few movie posters to go along with my suggestions...


    Kurosawa/Tonegawa: Rashomon and the Memory Engram



    David Lynch/Eric Kandel: Blue Velvet Aplysia


    Write-in nominations were allowed, too.



    How about Scorsese / Lynch / Maguire: Mulholland Taxi Driver's Hippocampus  {that one was a bit too involved for a poster}


    Finally, I'll write in one by David Cronenberg and....


    Cronenberg/Friston: Statistical Parametric Mapping to the Stars



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    2014

    Jessica is depressed again. After six straight weeks of overtime, her boss blandly praised her teamwork at the product launch party. And the following week she was passed over for a promotion in favor of Jason, her junior co-worker. "It's always that way, I'll never get ahead..."

    She arrives at her therapist's office late, looking stressed, disheveled, and dejected. The same old feelings of worthlessness and despair prompted her to resume her medication and CBT routine.

    "You deserve to be recognized for your work," said Dr. Harrison. "The things you're telling yourself right now are cognitive distortions: the black and white thinking, the overgeneralization, the self-blame, jumping to conclusions... " 

    "I guess so," muttered Jessica, looking down.

    "And you need a vacation!"
    . . .


    A brilliant suggestion, Dr. Harrison. As we all know, taking time off to relax and recharge after a stressful time will do wonders for our mental health. And building up a reserve of happy memories to draw upon during darker times is a cornerstone of positive psychology.

    Jessica and her husband Michael take a week-long vacation in Hawaii, creating new episodic memories that involve snorkling, parasailing, luaus, and mai tais on the beach. Jessica ultimately decides to quit her job and sell jewelry on Etsy.


    2015

    Michael is depressed after losing his job. His self-esteem has plummeted, and he feels useless. But he's too proud to ask for help. "Depression is something that happens to other people (like my wife), but not to me." He grows increasingly angry and starts drinking too much.

    Jessica finally convinces him to see Dr. Harrison's colleague. Dr. Roberts is a psychiatrist with a Ph.D. in neuroscience. She's adopted a translational approach and tries to incorporate the latest preclinical research into her practice. She's intrigued by the latest finding from Tonegawa's lab, which suggests that the reactivation of a happy memory is more effective in alleviating depression than experiencing a similar event in the present.

    Recalling happier memories can reverse depression, said the MIT press release. 

    So instead of telling Michael to take time off and travel and practice mindfulness and live in the present, she tells him to recall his fondest memory from last year's vacation in Hawaii.  

    It doesn't work.

    Michael goes to see Dr. Harrison, who prescribes bupropion and venlafaxine. Four weeks later, he feels much better, and starts a popular website that repudiates positive psychology. Seligman and Zimbardo are secretly chagrined. 

    . . .


    Happy Hippocampus
    photo credit: S. Ramirez


    Artificially reactivating positive [sexual] memories [in male mice] could offer an alternative to traditional antidepressantsmakes them struggle more when you hold them by the tail after 10 days of confinement.1

    Not as upbeat as the press release, eh?
    The findings ... offer a possible explanation for the success of psychotherapies in which depression patients are encouraged to recall pleasant experiences. They also suggest new ways to treat depression by manipulating the brain cells where memories are stored...

    “Once you identify specific sites in the memory circuit which are not functioning well, or whose boosting will bring a beneficial consequence, there is a possibility of inventing new medical technology where the improvement will be targeted to the specific part of the circuit, rather than administering a drug and letting that drug function everywhere in the brain,” says Susumu Tonegawa, ... senior author of the paper.

    Although this type of intervention is not yet possible in humans, “This type of analysis gives information as to where to target specific disorders,” Tonegawa adds.

    Before considering what the mice might actually experience when their happy memory cells are activated with light, let's all marvel at what was accomplished here.

    Ramirez et al. (2015) studied mice that were genetically engineered to allow blue light to activate a specific set of granule cells in the dentate gyrus subfield of the hippocampus. These neurons are critical for the formation of new memories and are considered “engram cells” that undergo physical changes and store discrete memories (Liu et al., 2014). When a cue reactivates the same set of neurons, the episodic memory is retrieved. In this study, the engram cells were part of a larger circuit that included the amygdala and the nucleus accumbens, regions important for processing emotion, motivation, and reward.

    Ramiriez, Liu, Tonegawa and colleagues have repeatedly demonstrated their masterful manipulation of mouse memories: activating fear memories, implanting false memories, and changing the valence of memories. These experiments are technically challenging and far outside my areas of expertise (greater detail in the Appendix below). In brief, the authors were able to label discrete sets of dentate gyrus cells while they were naturally activated during an interval of positive, neutral, or negative treatment. Then some groups of  animals were stressed for 10 days, and others remained in their home cages.


    The stressed mice exhibited signs of “depression-like” and “anxiety-like” behaviors.2  I'll spare you the long digression about whether the tail suspension test successfully models the anguished human experience of abject states, but you can read my earlier musings on the topic.


    The most astounding part of the experiment is that optical stimulation of positive-memory engram cells in stressed mice induced a reversal of “depressive” behaviors (but not “anxious” behaviors; see Appendix). Curiously, re-exposing the stressed male mice to an actual female did not have this positive benefit. So mediated experience artificial reactivation of the engram is even better than the real thing.

    The first author, graduate student Steve Ramirez, offered a post hocexplanation:
    “People who suffer from depression have those positive experiences in the brain, but the brain pieces necessary to recall them are broken. What we’re doing, in mice, is bypassing that circuitry and forcing it to be jump-started,” Ramirez says. “We’re harnessing the brain’s power from within itself and forcing the activation of that positive memory, whereas if you give a natural positive memory to the person or the animal, the depression that they have prevents them from finding that experience rewarding.”

    In other words, “We'll force you to be happy [i.e., possibly remember a positive experience], whether you like it or not.” And since the authors discussed therapeutic implications in the paper, they have to deal with the problem of phenomenology, whether they like it or not. What do the mice actually remember? Generic sexual experiences, a feeling of reward? An episodic-like memory, e.g. a specific act and all its spatiotemporal contextual information? Even if we allow mice to have “episodic-like” memories, the latter seems unlikely given the highly artificial and non-physiological method of neural stimulation that bypasses the precisely timed patterns of activity thought to “represent” past experience. These memory manipulation studies seem very futuristic and scary but Inception they are not.

    Our memories are plastic and malleable, and their physical instantiation changes each time we recall them. Which version of the Hawaii trip shall we target? What other memories show the greatest overlap with the happy one? Has the problem of hippocampal pattern separation been solved already?? Garden-variety deep brain stimulation seems easy in comparison (and we know how well that's gone in humans, so far). But: “In rodents, optogenetic stimulation of mPFC neurons, mPFC to raphe projections, and ventral tegmental dopaminergic neurons achieved a rapid reversal of stress-induced maladaptive behaviours” (Ramirez et al., 2015).

    Why can't we just appreciate the basic knowledge gained from these experiments? But no. There has to be a human application right around the corner.
    That link between the neural circuit manipulations in mice and therapies now used in humans makes the findings particularly exciting, says Tom Insel, director of the National Institute of Mental Health.

    “This is a big step toward helping to understand not only the underlying circuits for a really serious illness like depression, but also the circuits that underlie treatment,” says Insel...

    Was that actually an endorsement of mediated experience? If we go down that road, we must acknowledge that an artificially created reality, albeit one that originates within a being's own brain, is superior to real life. This is the most profound implication of activating positive memory engrams.


    When Mediated Experience Replaces a Medicated Existence
    Mediated experiences increasingly dominate our lives. Movies and television already confuse the real and the mediated. New technology is blurring the line further. Video games and virtual reality are becoming increasingly realistic. “Augmented reality” technology is on its way to the public. Wearable computers will allow people to enter a news story and see and feel the events the way the journalist who was there did and no doubt eventually we’ll be able to experience the events live. As the line between real and mediated gets harder to see, presence increases. An important and overlooked consequence of this trend is an increasing confusion from the other direction, in which “real life” seems to be mediated. People will have more and more trouble distinguishing reality, and some may not even appreciate that there is a difference. It will get harder for people to trust their own senses and judgment and it will be more difficult to impress people with non-mediated experiences.

    Reeves Timmins & Lombard (2005)When “Real” Seems Mediated: Inverse Presence.

    Heavy social media users already accept a reality filtered through Instagram and Facebook. As the interest in personal biometrics and the Quantified Self movement rises, so too will tolerance of increasingly invasive performance enhancing and “lifestyle” brain stimulation methods (see DIY tDCS). No one has said that optogenetic-type treatments are (or will be) possible in humans (OK, almost no one; see Albert, 2014). Others are more modest, and see the translational potential in non-invasive transcranial magnetic stimulation (Deisseroth et al., 2015).

    . . .


    2035

    DARPA has mandated that all depressed Americans must be implanted with its CyberNeuroTron WritBit device, which cost $100 billion to develop. CNTWB is a closed-loop DBS system that automatically adjusts the stimulation parameters at 12 different customized target locations. It uses state-of-the-art syringe-injectable mesh electronics, incorporating silicon nanowires and microvoltammetry. Electrical and chemical signals are continuously recorded and uploaded to a centralized data center, where machine learning algorithms determine with high accuracy whether a given pattern of activity signals a significant change in mood.

    The data are compiled, analyzed, and stored by the global search engine conglomerate BlueBook, which in 2032 swallowed up Google, Facebook, Apple, and every other internet data mining company.



    . . .


    2055

    Sophia, the daughter of Jessica and Michael, is depressed again. The Ramirez et al. (2050) protocol for Positive Memory Engram Activation is in widespread use. Sophia searches for her dentate gyrus recordings from a vacation in Hawaii five months earlier. Then she selects the specific memory she wants to be artificially reactivated: watching the sunset on the beach with her partner, drinking mai tais and eating taro chips.



    "We had a great time on that trip, didn't we Lucas?" 

    Lucas the intelligent AI nods in agreement. "It's true," he thought. "Humans can no longer distinguish between virtual reality and the real thing."

    This has been especially useful for the Ramirez protocol, since most Pacific Island nations have been underwater since 2047.



    Footnotes

    1 As an aside, I wonder what the female mice think of all this. What would be an equivalently positive experience? Is sex as rewarding for them? Will there be a new animal model of shopping at Nordstrom? Fortunately, this work was funded by RIKEN Brain Science Institute and Howard Hughes Medical Institute, so the authors don't have to follow the pesky impending NIH guidelines to include females in animal research.

    2“Depression-related” behaviors were assessed using the Tail Suspension Test (TST) and the Sucrose Preference Test (SPT), which are supposed to mimic giving up hope and loss of pleasure, respectively. Different tests were used to measure “anxiety-related” behaviors. Interestingly, none of the happy engram manipulations improved anxiety-like behavior in the mice. Not a very good model of anxious depression, then.


    References

    Albert PR. (2014). Light up your life: optogenetics for depression?J Psychiatry Neurosci. 39(1):3-5.

    Deisseroth K, Etkin A, Malenka RC. (2015). Optogenetics and the circuit dynamics ofpsychiatric disease. JAMA 313(20):2019-20.

    Liu, X., Ramirez, S., Redondo, R., & Tonegawa, S. (2014). Identification and Manipulation of Memory Engram Cells Cold Spring Harbor Symposia on Quantitative Biology, 79, 59-65. DOI: 10.1101/sqb.2014.79.024901

    Ramirez, S., Liu, X., MacDonald, C., Moffa, A., Zhou, J., Redondo, R., & Tonegawa, S. (2015). Activating positive memory engrams suppresses depression-like behaviour. Nature, 522 (7556), 335-339. DOI: 10.1038/nature14514

    Timmins, L., & Lombard, M. (2005). When “Real” Seems Mediated: Inverse Presence. Presence: Teleoperators and Virtual Environments, 14 (4), 492-500. DOI: 10.1162/105474605774785307


    Appendix

    These experiments are indeed difficult, but if you successfully execute them, a publication is Nature nearly guaranteed. A review by Liu et al. (2014) explained their general protocol in an easier-to-understand fashion:
    ...we combined activity-dependent, drug-regulatable expression system with optogenetics (Liu et al. 2012). We used a transgenic mouse model where the artificial tetracycline transactivator (tTA), which can be blocked by doxycycline (Dox), is driven by the promoter of immediate early gene (IEG) c-fos (Reijmers et al. 2007). The activity dependency of c-fos promoter poses a natural spatial constrain on the identities of the neurons that can be labeled, reflecting the normal biological selection process of the brain during memory formation, whereas the Dox-dependency of the system poses an artificial temporal constrain as to when these neurons can be labeled, which can be controlled by the experimenters. With these two constraints, the down-stream effector of tTA can express selectively in neurons that are active during a particular behavior episode, only if the animals are off Dox diet. Using this system, we expressed channelrhodopsin-2 (ChR2) delivered by a viral vector AAV-TRE-ChR2-EYFP targeting the dentate gyrus (DG) of the hippocampus and implanted optical fibers right above the infected areas. 

    One of the major treatment protocols is shown below (adapted from Fig. 1A).



    There were a number of control conditions too. Reactivation of neutral or negative engram neurons didn't change depression-like behaviors on the TST and SPT.  Reactivation of positive engram neurons in non-stressed mice didn't alter behavior, either.



    A very impressive body of work, with a special dedication by the authors: "We dedicate this study to the memory of Xu Liu, who made major contributions to memory engram research."

    Xu Liu in memoriam.


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    Recently, Science and Nature had news features on big BRAIN funding for the development of deep brain stimulation technologies. The ultimate aim of this research is to treat and correct malfunctioning neural circuits in psychiatric and neurological disorders. Both pieces raised ethical issues, focused on device manufacturers and potential military applications, respectively.

    A different ethical concern, not mentioned in either article, is who will have access to these new devices, and who is going to pay the medical costs once they hit the market. DBS for movement disorders is a test case, because Medicare (U.S.) approved coverage for Parkinson's disease (PD) and essential tremor in 2003. Which is good, given that unilateral surgery costs about $50,000.

    Willis et al. (2014) examined Medicare records for 657,000 PD patients and found striking racial disparities. The odds of receiving DBS in white PD patients were five times higher than for African Americans, and 1.8 times higher than for Asians. And living in a neighborhood with high socioeconomic status was associated with 1.4-fold higher odds of receiving DBS. Out-of-pocket costs for Medicare patients receiving DBS are over $2,000 per year, which is quite a lot of money for low-income senior citizens.

    Aaron Saenz raised a similar issue regarding the cost of the DEKA prosthetic arm (aka "Luke"):
    But if you're not a veteran, neither DARPA project may really help you much. The Luke Arm is slated to cost $100,000+.... That's well beyond the means of most amputees if they do not have the insurance coverage provided by the Veteran's Administration. ... As most amputees are not veterans, I think that the Luke Arm has a good chance of being priced out of a large market share.

    The availability of qualified neurosurgeons, even in affluent areas, will be another problem once future indications are FDA-approved (or even trialed).

    The situation in one Canadian province (British Columbia, with a population of 4.6 million) is instructive. An article in the Vancouver Sun noted that in March 2013, only one neurosurgeon was qualified to perform DBS surgeries for Parkinson's disease (or for dystonia). This resulted in a three year waiting list. Imagine, all these eligible patients with Parkinson's have to endure their current condition (and worse) for years longer, instead of having a vastly improved quality of life.
    Funding, doctors needed if brain stimulation surgery to expand in B.C.:

    ... “But here’s the problem: We already have a waiting list of almost three years, from the time family doctors first put in the referral to the DBS clinic. And I’m the only one in B.C. doing this. So we really aren’t able to do more than 40 cases a year,” [Dr. Christopher Honey] said.
    . . .
    ...The health authority allocates funding of $1.1 million annually, which includes the cost of the $20,000 devices, and $14,000 for each battery replacement. On average, batteries need to be replaced every three years.
    . . .
    To reduce wait times, the budget would have to increase and a Honey clone would have to be trained and hired.

    Back in the U.S., Rossi et al. (2014) called out Medicare for curbing medical progress:
    Devices for DBS have been approved by the FDA for use in treating Parkinson disease, essential tremor, obsessive-compulsive disorder, and dystonia,2 but expanding DBS use to include new indications has proven difficult—specifically because of the high cost of DBS devices and generally because of disincentives for device manufacturers to sponsor studies when disease populations are small and the potential for a return on investment is not clear. In many of these cases, Medicare coverage will determine whether a study will proceed. ... Ultimately, uncertain Medicare coverage coupled with the lack of economic incentives for industry sponsorship could limit investigators’ freedom of inquiry and ability to conduct clinical trials for new uses of DBS therapy.

    But the question remains, where is all this health care money supposed to come from?

    The device manufacturers aren't off the hook, either, but BRAIN is trying to reel them in. NIH recently sponsored a two-day workshop, BRAIN Initiative Program for Industry Partnerships to Facilitate Early Access Neuromodulation and Recording Devices for Human Clinical Studies [agenda PDF]. The purpose was to:
    • Bring together stakeholders and interested parties to disseminate information on opportunities for research using latest-generation devices for CNS neuromodulation and interfacing with the brain in humans.
    • Describe the proposed NIH framework for facilitating and lowering the cost of new studies using these devices.
    • Discuss regulatory and intellectual property considerations.
    • Solicit recommendations for data coordination and access.

    The Program Goals [PDF]:
    ...we hope to spur human research bridging the “valley of death” that has been a barrier to translating pre-clinical research into therapeutic outcomes. We expect the new framework will allow academic researchers to test innovative ideas for new therapies, or to address scientific unknowns regarding mechanisms of disease or device action, which will facilitate the creation of solid business cases by industry and venture capital for the larger clinical trials required to take these ideas to market.

    To advance these goals, NIH is pursuing general agreements (Memoranda of Understanding, MOUs) with device manufacturers to set up a framework for this funding program. In the MOUs, we expect each company to specify the capabilities of their devices, along with information, support and any other concessions they are willing to provide to researchers.

    In other words, it's a public/private partnership to advance the goal of having all depressed Americans implanted with the CyberNeuroTron WritBit device by 2035 (just kidding!!).

    But seriously... before touting the impending clinical relevance of a study in rodents, basic scientists and bureaucrats alike should listen to patients with the current generation of DBS devices. Participants in the halted BROADEN Trial for refractory depression reported outcomes ranging from “...the side effects caused by the device were, at times, worse than the depression itself” to “I feel like I have a second chance at life.”

    What do you do with a medical device that causes great physical harm to one person but is a godsend for another? What are the factors involved? Sloppy patient selection criteria? Surgeon ineptitude? Anatomical variation? All of the above and more are likely to contribute to the wildly divergent outcomes.

    One anonymous commenter on a previous post recently said that the study sponsor had abandoned them:
    The BROADEN study isn't continuing the 4 year follow-up study. I'm in it and just got a phone call. They'll put in a rechargeable device for those of us enrolled and will not follow up with us. The FDA approved it just for us who had the surgery. It looks like St. Judes isn't going foe FDA approval anymore. I have no public reference for this but it was what I was just told over the phone. It has helped me and I don't know what I'm going to do about follow-up care except with my psychiatrist who doesn't have DBS experience. Scary.

    Why isn't the manufacturer providing medical care for the study participants? Because they don't have to! In her Science piece, Emily Underwood reported:
    Recent failures of several large clinical trials of deep brain stimulation for depression loomed large over the meeting. In the United States, companies or institutions sponsoring research are rarely, if ever, required to pay medical costs that trial subjects incur as a result of their participation, [Hank] Greely points out. “Many people who work in research ethics, including me, think this is wrong,” he says. 

    Hopefully the workshop attendees considered not only how to lower the cost of new DBS studies, but also how to provide equitable circuit-based health care in the future.


    Further Reading (and viewing)

    Watch the NIH videocast: Day 1 and Day 2.

    BROADEN Trial of DBS for Treatment-Resistant Depression

    Update on the BROADEN Trial of DBS for Treatment-Resistant Depression


    References

    Rossi, P., Machado, A., & Okun, M. (2014). Medicare Coverage of Investigational Devices. JAMA Neurology, 71 (5) DOI: 10.1001/jamaneurol.2013.6042

    Willis, A., Schootman, M., Kung, N., Wang, X., Perlmutter, J., & Racette, B. (2014). Disparities in deep brain stimulation surgery among insured elders with Parkinson disease. Neurology, 82 (2), 163-171 DOI: 10.1212/WNL.0000000000000017


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    For some inexplicable reason, you watched the torture gore horror film Hostel over the weekend. On Monday, you're having trouble concentrating at work. Images of severed limbs and bludgeoned heads keep intruding on your attempts to code or write a paper. So you decide to read about the making of Hostel.You end up seeing pictures of the most horrifying scenes from the movie. It's all way too way much to simply shake off so then you decide to play Tetris.

    But a funny thing happens. The unwelcome images start to become less frequent. By Friday, the gory mental snapshots are no longer forcing their way into your mind's eye. The ugly flashbacks are gone.

    Meanwhile, your parnter in crime is having similar images of eye gouging pop into his head. Except he didn't review the tortuous highlights on Monday, and he didn't play Tetris. He continues to have involuntary intrusions of Hostel images once or twice a day for the rest of the week.

    This is basically the premise (and outcome) of a new paper in Psychological Science by Ella James and colleagues at Cambridge and Oxford. It builds on earlier work suggesting that healthy participants who play Tetris shortly after watching a “trauma” film will have fewer intrusive memories (Holmes et al, 2009, 2010). This is based on the idea that involuntary “flashbacks” in real post-traumatic stress disorder (PTSD) are visual in nature, and require visuospatial processing resources to generate and maintain. Playing Tetris will interfere with consolidation and subsequent intrusion of the images, at least in an experimental setting (Holmes et al, 2009):
    ...Traumaflashbacks are sensory-perceptual, visuospatial mental images. Visuospatial cognitive tasks selectively compete for resources required to generate mental images. Thus, a visuospatial computergame (e.g. "Tetris") will interfere with flashbacks. Visuospatial tasks post-trauma, performed within the time window for memory consolidation [6 hrs], will reduce subsequent flashbacks. We predicted that playing"Tetris" half an hour after viewing trauma would reduce flashback frequency over 1-week.

    The timing is key here. In the earlier experiments, Tetris play commenced 30 min after the trauma film experience, during the 6 hour window when memories for the event are stabilized and consolidated. Newly formed memories are thought to be malleable during this time.

    However, if one wants to extrapolate directly to clinical application in cases of real life trauma exposure (and this is problematic, as we'll see later), it's pretty impractical to play Tetris right after an earthquake, auto accident, mortar attack, or sexual assault. So the new paper relies on the process of reconsolidation, when an act of remembering will place the memory in a labile state once again, so it can be modified (James et al., 2015).




    The procedure was as follows: 52 participants came into the lab on Day 0 and completed questionnaires about depression, anxiety, and previous trauma exposure. Then they watched a 12 min trauma film that included 11 scenes of actual death (or threatened death) or serious injury (James et al., 2015):
    ...the film functioned as an experimental analogue of viewing a traumatic event in real life. Scenes contained different types of context; examples include a young girl hit by a car with blood dripping out of her ear, a man drowning in the sea, and a van hitting a teenage boy while he was using his mobile phone crossing the road. This film footage has been used in previous studies to evoke intrusive memories...

    After the film, they rated “how sad, hopeless, depressed, fearful, horrified, and anxious they felt right at this very moment” and “how distressing did you find the film you just watched?” They were instructed to keep a diary of intrusive images and come back to the lab 24 hours later.

    On Day 1, participants were randomized to either the experimental group (memory reactivation + Tetris) or the control group (neither manipulation). The experimental group viewed 11 still images from the film that served as reminder cues to initiate reconsolidation. This was followed by a 10 min filler task and then 12 min of playing Tetris (the Marathon mode shown above). The game instructions aimed to maximize the amount of mental rotation the subjects would use. The controls did the filler task and then sat quietly for 12 min.

    Both groups kept a diary of intrusions for the next week, and then returned on Day 7. All participants performed the Intrusion Provocation Task (IPT). Eleven blurred pictures from the film were shown, and subjects indicated when any intrusive mental images were provoked. Finally, the participants completed a few more questionnaires, as well as a recognition task that tested their verbal (T/F written statements) and visual (Y/N for scenes) memories of the film.1

    The results indicated that the Reactivation + Tetris manipulation was successful in decreasing the number of visual memory intrusions in both the 7-day diary and the IPT (as shown below).


    modified from Fig. 1 (James et al., 2015). Asterisks indicate a significant difference between groups (**p < .001). Error bars represent +1 SEM.


    Cool little snowman plots (actually frequency scatter plots) illustrate the time course of intrusive memories in the two groups.


    modified from Fig. 2 (James et al., 2015). Frequency scatter plots showing the time course of intrusive memories reported in the diary daily from Day 0 (prior to intervention) to Day 7. The intervention was on Day 1, and the red arrow is 24 hrs later (when the intervention starts working). The solid lines are the results of a generalized additive model. The size of the bubbles represents the number of participants who reported the indicated number of intrusive memories on that particular day.


    But now, you might be asking yourself if the critical element was Tetris or the reconsolidation update procedure (or both), since the control group did neither. Not to worry. Experiment 2 tried to disentangle this by recruiting four groups of participants (n=18 in each) the original two groups plus two new ones: Reactivation only and Tetris only.

    And the results from Exp. 2 demonstrated that both were needed.


    modified from Fig. 4 (James et al., 2015). Asterisks indicate that results for the Reactivation + Tetris group were significantly different from results for the other three groups (*p < .01). Error bars represent +1 SEM. The No-Task Control and Tetris Only groups did not differ for diary intrusions (n.s.).


    The authors' interpretation:
    Overall, the results of the present experiments indicate that the frequency of intrusive memories induced by experimental trauma can be reduced by disrupting reconsolidation via a competing cognitive-task procedure, even for established memories (here, events viewed 24 hours previously). ... Critically, neither playing Tetris alone (a nonreactivation control condition) nor the control of memory reactivation alone was sufficient to reduce intrusions... Rather, their combination is required, which supports a reconsolidation-theory account. We suggest that intrusive-memory reduction is due to engaging in a visuospatial task within the window of memory reconsolidation, which interferes with intrusive image reconsolidation (via competition for shared resources).

    Surprisingly (perhaps), I don't have anything negative to say about the study. It was carefully conducted and interpreted with restraint. They don't overextrapolate to PTSD. They don't use the word “flashback” to describe the memory phenomenon. And they repeatedly point out that it's “experimental trauma.” I actually considered reviving The Neurocomplimenter for this post, but that would be going too far...

    Compare this flattering post with one I wrote in 2010, about a related study by the same authors (Holmes et al.. 2010). That paper certainly had a modest title: Key Steps in Developing a Cognitive Vaccine against Traumatic Flashbacks: Visuospatial Tetris versus Verbal Pub Quiz.

    Cognitive vaccine. Traumatic. Flashbacks. Twelve mentions of PTSD. This led to ridiculous headlines like Doctors Prescribing 'Tetris Therapy'.

    Here, let me fix that for you:

    Tetris Helps Prevent Unpleasant Memories of Gory Film in Happy People

    My problem wasn't with the actual study, but with the way the authors hyped the results and exaggerated their clinical significance. So I'm pleased to see a more restrained approach here.


    The media coverage for the new paper was generally more accurate too:

    Can playing Tetris reduce intrusive memories? (Medical News Today)

    Moving tiles as an unintrusive way to handle flashbacks (Medical Express)

    Intrusiveness of Old Emotional Memories Can Be Reduced by Computer Game Play Procedure (APS)

    But we can always count on the Daily Mail for a good time: Could playing TETRIS banish bad memories? Retro Nintendo game 'reduces the risk of post-traumatic stress disorder'2

    Gizmodo is a bit hyperbolic as well: Tetris Blocks Flashbacks of Traumatic Events Lodged in the Brain [“lodged in the brain” for all of 24 hrs]


    Questions for Now and the Future

    Is there really nothing wrong with this study?? Being The Neurocritic, I always have to find something to criticize... and here I had to dig through the Supplemental Material to find issues that may affect the translational potential of Tetris-based interventions.

    • The Intrusion subscale of the Impact of Event Scale (IES-R) was used as an exploratory measure, and subject ratings were between 0 and 1.
    The Intrusion subscale consists of 8 questions like “I found myself acting or feeling like I was back at that time” and “I had dreams about it” that are rated from 0 (not at all) to 4 (extremely). The IES-R is given to people after distressing, traumatic life events. These individuals may have actual PTSD symptoms like flashbacks and nightmares.

    In Exp. 1, the Reactivation + Tetris group (M = .68) had significantly lower scores (p = .016) on Day 7 than the control group (M = 1.01). BUT this is not terribly meaningful, due to a floor effect. And in Exp. 2 there was no difference between the four groups, with scores ranging from 0.61 to 0.81.3

    As an overall comment, watching a film of a girl getting hit by a car is not the same as witnessing it in person (obviously). But this real-life scenario may be the most amenable to Tetris, because the witness was not in the accident themselves and did not know the girl (both of which would heighten the emotional intensity and vividness of the trauma, elements that transcend visual imagery).

    It's true that in PTSD, the involuntary intrusion of trauma memories (i.e., flashbacks) have a distinctly sensory quality to them (Ehlers et al. 2004). Visual images are most common, but bodily sensations, sounds, and smells can be incorporated into a multimodal flashback. Or could occur on their own.

    • The effectiveness of the Tetris intervention was related to game score and self-rated task difficulty.
    This means that people who were better at playing Tetris showed a greater decrease in intrusive memories. This result wasn't covered in the main paper, but it makes you wonder about cause and effect. Is it because the game was more enjoyable for them? Or could it be that their superior visual-spatial abilities (or greater game experience) resulted in greater interference, perhaps by using up more processing resources? That's always a dicey argument, as you could also predict that better, more efficient game play uses fewer visual-spatial resources.

    An interesting recent paper found that individuals with PTSD (who presumably experience intrusive visual memories) have worse allocentric spatial processing abilities than controls (Smith et al., 2015). This means they have problems representing the locations of environmental features relative to each other (instead of relative to the self). So are weak spatial processing and spatial memory abilities caused by the trauma, or are weak spatial abilities a vulnerability factor for developing PTSD?

    • As noted by the authors, the modality-specificity of the intervention needs to be assessed.
    Their previous paper showed that the effect was indeed specific to Tetris. A verbally based video game (Pub Quiz) actually increased the frequency of intrusive images (Holmes et al., 2010).

    It would be interesting to disentangle the interfering elements of Tetris even further. Would any old mental rotation task do the trick? How about passive viewing of Tetris blocks, or is active game play necessary? Would a visuospatial n-back working memory task work? It wouldn't be as fun, but it obviously uses up visual working memory processing resources. What about Asteroids or Pac-Man or...? 4

    This body of work raises a number of interesting questions about the nature of intrusive visual memories, traumatic and non-traumatic alike. Do avid players of action video games (or Tetris) have fewer intrusive memories of past trauma or trauma-analogues in everyday life? I'm not sure this is likely, but you could find out pretty quickly on Amazon Mechanical Turk or one of its alternatives.

    There are also many hurdles to surmount before Doctors Prescribe 'Tetris Therapy'. For instance, what does it mean to have the number of weekly Hostel intrusions drop from five to two? How would that scale to an actual trauma flashback, which may involve a fear or panic response?

    The authors conclude the paper by briefly addressing these points:
    A critical next step is to  investigate  whether  findings  extend  to  reducing  the psychological impact of real-world emotional events and media. Conversely, could computer gaming be affecting intrusions of everyday events?

    A number of different research avenues await these investigators (and other interested parties). And — wait for it — a clinical trial of Tetris for flashback reduction has already been completed by the investigators at Oxford and Cambridge!

    A Simple Cognitive Task to Reduce the Build-Up of Flashbacks After a Road Traffic Accident (SCARTA)

    Holmes and colleagues took the consolidation window very seriously: participants played Tetris in the emergency room within 6 hours of experiencing or witnessing an accident. I'll be very curious to see how this turns out...


    Footnotes

    1 Interestingly, voluntary retrieval of visual and verbal memories was not affected by the manipulation, highlighting the uniqueness of flashback-like phenomena.

    2 It does no such thing. But they did embed a video of Dr. Tom Stafford explaining why Tetris is so compelling...

    3 The maximum total score on the IES-R is 32. The mean total score in a group of car accident survivors was 17; in Croatian war veterans it was 25. At first I assumed the authors reported the total score out of 32, rather than the mean score per item. I could be very wrong, however. By way of comparison, the mean item score in female survivors of intimate partner violence was 2.26. Either way, the impact of the trauma film was pretty low in this study, as you might expect.

    4 OK, now I'm getting ridiculous. I'm also leaving aside modern first-person shooter games as potentially too traumatic and triggering.


    References

    Ehlers A, Hackmann A, Michael T. (2004). Intrusive re-experiencing in post-traumaticstress disorder: phenomenology, theory, and therapy. Memory 12(4):403-15.

    Holmes EA, James EL, Coode-Bate T, Deeprose C. (2009). Can playing the computer game "Tetris" reduce the build-up of flashbacks for trauma? A proposal from cognitive science. PLoS One 4(1):e4153.

    Holmes, E., James, E., Kilford, E., & Deeprose, C. (2010). Key Steps in Developing a Cognitive Vaccine against Traumatic Flashbacks: Visuospatial Tetris versus Verbal Pub Quiz. PLoS ONE, 5 (11) DOI: 10.1371/journal.pone.0013706

    James, E., Bonsall, M., Hoppitt, L., Tunbridge, E., Geddes, J., Milton, A., & Holmes, E. (2015). Computer Game Play Reduces Intrusive Memories of Experimental Trauma via Reconsolidation-Update Mechanisms. Psychological Science DOI: 10.1177/0956797615583071

    Smith KV, Burgess N, Brewin CR, King JA. (2015). Impaired allocentric spatialprocessing in posttraumatic stress disorder. Neurobiol Learn Mem. 119:69-76.


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    In case you've been living under a rock the past few weeks, Google's foray into artificial neural networkshas yielded hundreds of thousands of phantasmagoric images. The company has an obvious interest in image classification, and here's how they explain the DeepDream process in their Research Blog:
    Inceptionism: Going Deeper into Neural Networks

    . . .
    We train an artificial neural network by showing it millions of training examples [of dogs and eyes and pagodas, let's say] and gradually adjusting the network parameters until it gives the classifications we want. The network typically consists of 10-30 stacked layers of artificial neurons. Each image is fed into the input layer, which then talks to the next layer, until eventually the “output” layer is reached. The network’s “answer” comes from this final output layer.

    . . .
    One way to visualize what goes on is to turn the network upside down and ask it to enhance an input image in such a way as to elicit a particular interpretation. Say you want to know what sort of image would result in “Banana.” Start with an image full of random noise, then gradually tweak the image towards what the neural net considers a banana... By itself, that doesn’t work very well, but it does if we impose a prior constraint that the image should have similar statistics to natural images, such as neighboring pixels needing to be correlated.

    After Google released the deepdream code on GitHub, Psychic VR Lab set up a Deep Dream web interface, which currently has over 300,000 groovy and scary images.

    I've taken an interest in the hallucinogenic and distorted brain images, including the one above. I can't properly credit the human input interface (which wasn't me), but I found it after a submitting a file of my own in the early stages of http://psychic-vr-lab.com/deepdream/.  I can't find the url hosting my image, but I came across the frightening brain here, along with the original.





    I've included a few more for your viewing pleasure. Brain Decoder posted a dreamy mouse hippocampus Brainbow.




    Here's one by HofmannsBicycle.



    And a fun fave courtesy of @rogierK and @katestorrs. This one is cartoonish instead of menacing.



    Rogier said: "According to #deepdream the homunculus in our brains is a terrifying bird-dog hybrid."

    Aw, I thought it was kind of cute. More small birds, fewer staring judgmental eyeballs.


    And the grand finale isn't a brain at all. But who doesn't want to see the dreamified version of The Garden of Earthly Delights, by Hieronymus Bosch? Here it is, via @aut0mata. Click on image for a larger view.
     




    When nothing's right, just close your eyes
    Close your eyes and you're gone

    -Beck, Dreams



    Warnning: Do NOT Get Caught While Searching!!
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    R2D3 recently had a fantastic Visual Introduction to Machine Learning, using the classification of homes in San Francisco vs. New York as their example. As they explain quite simply:
    In machine learning, computers apply statistical learning techniques to automatically identify patterns in data. These techniques can be used to make highly accurate predictions.
    You should really head over there right now to view it, because it's very impressive.


    Computational neuroscience types are using machine learning algorithms to classify all sorts of brain states, and diagnose brain disorders, in humans. How accurate are these classifications? Do the studies all use separate training sets and test sets, as shown in the example above?

    Let's say your fMRI measure is able to differentiate individuals with panic disorder (n=33) from those with panic disorder + depression (n=26) with 79% accuracy.1 Or with structural MRI scans you can distinguish 20 participants with treatment-refractory depression from 21 never-depressed individuals with 85% accuracy.2 Besides the issues outlined in the footnotes, the reality check is that the model must be able to predict group membership for a new (untrained) data set. And most studies don't seem to do this.

    I was originally drawn to the topic by a 3 page article entitled, Machine learning algorithm accurately detects fMRI signature of vulnerability to major depression (Sato et al., 2015). Wow! Really? How accurate? Which fMRI signature? Let's take a look.
    • machine learning algorithm = Maximum Entropy Linear Discriminant Analysis (MLDA)
    • accurately predicts = 78.3% (72.0% sensitivity and 85.7% specificity)
    • fMRI signature = guilt-selective anterior temporal functional connectivity changes (seems a bit overly specific and esoteric, no?)
    • vulnerability to major depression = 25 participants with remitted depression vs. 21 never-depressed participants
    The authors used a standard leave-one-subject-out procedure in which the classification is cross-validated iteratively by using a model based on the sample after excluding one subject to independently predict group membershipbut they did not test their fMRI signature in completely independent groups of participants.

    Nor did they try to compare individuals who are currently depressed to those who are currently remitted. That didn't matter, apparently, because the authors suggest the fMRI signature is a trait markerof vulnerability, not a state marker of current mood. But the classifier missed 28% of the remitted group who did not have the guilt-selective anterior temporal functional connectivity changes.”

    What is that, you ask? This is a set of mini-regions (i.e., not too many voxels in each) functionally connected to a right superior anterior temporal lobe seed region of interest during a contrast of guilt vs. anger feelings (selected from a number of other possible emotions) for self or best friend, based on written imaginary scenarios like “Angela [self] does act stingily towards Rachel [friend]” and “Rachel does act stingily towards Angela” conducted outside the scanner (after the fMRI session is over). Got that?

    You really need to read a bunch of other articles to understand what that means, because the current paper is less than 3 pages long. Did I say that already?


    modified from Fig 1B (Sato et al., 2015). Weight vector maps highlighting voxels among the 1% most discriminative for remitted major depression vs. controls, including the subgenual cingulate cortex, both hippocampi, the right thalamus and the anterior insulae.


    The patients were previously diagnosed according to DSM-IV-TR (which was current at the time), and in remission for at least 12 months. The study was conducted by investigators from Brazil and the UK, so they didn't have to worry about RDoC, i.e. “new ways of classifying mental disorders based on behavioral dimensions and neurobiological measures” (instead of DSM-5 criteria). A “guilt-proneness” behavioral construct, along with the “guilt-selective” network of idiosyncratic brain regions, might be more in line with RDoC than past major depression diagnosis.

    Could these results possibly generalize to other populations of remitted and never-depressed individuals? Well, the fMRI signature seems a bit specialized (and convoluted). And overfitting is another likely problem here...

    In their next post, R2D3 will discuss overfitting:
    Ideally, the [decision] tree should perform similarly on both known and unknown data.

    So this one is less than ideal. [NOTE: the one that's 90% in the top figure]

    These errors are due to overfitting. Our model has learned to treat every detail in the training data as important, even details that turned out to be irrelevant.

    In my next post, I'll present an unsystematic review of machine learning as applied to the classification of major depression. It's notable that Sato et al. (2015) used the word “classification” instead of “diagnosis.”3


    ADDENDUM (Aug 3 2015): In the comments, I've presented more specific critiques of: (1) the leave-one-out procedure and (2) how the biomarker is temporally disconnected from when the participants identify their feeling as 'guilt' or 'anger' or etc. (and why shame is more closely related to depression than guilt).


    Footnotes

    1 The sensitivity (true positive rate) was 73% and the specificity (true negative rate) was 85%. After correcting for confounding variables, these numbers were 77% and 70%, respectively.

    2 The abstract concludes this is a “high degree of accuracy.” Not to pick on these particular authors (this is a typical study), but Dr. Dorothy Bishop explains why this is not very helpful for screening or diagnostic purposes. And what you'd really want to do here is to discriminate between treatment-resistant vs. treatment-responsive depression. If an individual does not respond to standard treatments, it would be highly beneficial to avoid a long futile period of medication trials.

    3 In case you're wondering, the title of this post was based on The Dark Side of Diagnosis by Brain Scan, which is about Dr  Daniel Amen. The work of the investigators discussed here is in no way, shape, or form related to any of the issues discussed in that post.


    Reference

    Sato, J., Moll, J., Green, S., Deakin, J., Thomaz, C., & Zahn, R. (2015). Machine learning algorithm accurately detects fMRI signature of vulnerability to major depression Psychiatry Research: Neuroimaging DOI: 10.1016/j.pscychresns.2015.07.001


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    How do we classify and diagnose mental disorders?

    In the coming era of Precision Medicine, we'll all want customized treatments that “take into account individual differences in people’s genes, environments, and lifestyles.” To do this, we'll need precise diagnostic tools to identify the specific disease process in each individual. Although focused on cancer in the near-term, the longer-term goal of the White House initiative is to apply Precision Medicine to all areas of health. This presumably includes psychiatry, but the links between Precision Medicine, the BRAIN initiative, and RDoC seem a bit murky at present.1

    But there's nothing a good infographic can't fix. Science recently published a Perspective piece by the NIMH Director and the chief architect of the Research Domain Criteria (RDoC) initiative (Insel & Cuthbert, 2015). There's Deconstruction involved, so what's not to like? 2


    ILLUSTRATION: V. Altounian and C. Smith / SCIENCE


    In this massively ambitious future scenario, the totality of one's genetic risk factors, brain activity, physiology, immune function, behavioral symptom profile, and life experience (social, cultural, environmental) will be deconstructed and stratified and recompiled into a neat little cohort. 3

    The new categories will be data driven. The project might start by collecting colossal quantities of expensive data from millions of people, and continue by running classifiers on exceptionally powerful computers (powered by exceptionally bright scientists/engineers/coders) to extract meaningful patterns that can categorize the data with high levels of sensitivity and specificity. Perhaps I am filled with pathologically high levels of negative affect (Loss? Frustrative Nonreward?), but I find it hard to be optimistic about progress in the immediate future. You know, for a Precision Medicine treatment for me (and my pessimism)...

    But seriously.

    Yes, RDoC is ambitious (and has its share of naysayers). But what you may not know is that it's also trendy! Just the other day, an article in The Atlantic explained Why Depression Needs A New Definition (yes, RDoC) and even cited papers like Depression: The Shroud of Heterogeneity. 4

    But let's just focus on the brain for now. For a long time, most neuroscientists have viewed mental disorders as brain disorders. [But that's not to say that environment, culture, experience, etc. play no role! cf. Footnote 3]. So our opening question becomes, How do we classify and diagnose brain disorders neural circuit disorders in a fashion consistent with RDoC principles? Is there really One Brain Network for All Mental Illness, for instance? (I didn't think so.)

    Our colleagues in Asia and Australia and Europe and Canada may not have gotten the funding memo, however, and continue to run classifiers based on DSM categories. 5 In my previous post, I promised an unsystematic review of machine learning as applied to the classification of major depression. You can skip directly to the Appendix to see that.

    Regardless of whether we use DSM-5 categories or RDoC matrix constructs, what we need are robust and reproducible biomarkers (see Table 1 above). A brief but excellent primer by Woo and Wager (2015) outlined the characteristics of a useful neuroimaging biomarker:
    1. Criterion 1: diagnosticity

    Good biomarkers should produce high diagnostic performance in classification or prediction. Diagnostic performance can be evaluated by sensitivity and specificity. Sensitivity concerns whether a model can correctly detect signal when signal exists. Effect size is a closely related concept; larger effect sizes are related to higher sensitivity. Specificity concerns whether the model produces negative results when there is no signal. Specificity can be evaluated relative to a range of specific alternative conditions that may be confusable with the condition of interest.

    2. Criterion 2: interpretability

    Brain-based biomarkers should be meaningful and interpretable in terms of neuroscience, including previous neuroimaging studies and converging evidence from multiple sources (eg, animal models, lesion studies, etc). One potential pitfall in developing neuroimaging biomarkers is that classification or prediction models can capitalize on confounding variables that are not neuroscientifically meaningful or interesting at all (eg, in-scanner head movement). Therefore, neuroimaging biomarkers should be evaluated and interpreted in the light of existing neuroscientific findings.

    3. Criterion 3: deployability

    Once the classification or outcome-prediction model has been developed as a neuroimaging biomarker, the model and the testing procedure should be precisely defined so that it can be prospectively applied to new data. Any flexibility in the testing procedures could introduce potential overoptimistic biases into test results, rendering them useless and potentially misleading. For example, “amygdala activity” cannot be a good neuroimaging biomarker without a precise definition of which “voxels” in the amygdala should be activated and the relative expected intensity of activity across each voxel. A well-defined model and standardized testing procedure are crucial aspects of turning neuroimaging results into a “research product,” a biomarker that can be shared and tested across laboratories.

    4. Criterion 4: generalizability

    Clinically useful neuroimaging biomarkers aim to provide predictions about new individuals. Therefore, they should be validated through prospective testing to prove that their performance is generalizable across different laboratories, different scanners or scanning procedures, different populations, and variants of testing conditions (eg, other types of chronic pain). Generalizability tests inherently require multistudy and multisite efforts. With a precisely defined model and standardized testing procedure (criterion 3), we can easily test the generalizability of biomarkers and define the boundary conditions under which they are valid and useful.
    [Then the authors evaluated the performance of a structural MRI signature for IBS presented in an accompanying paper.]

    Should we try to improve on a neuroimaging biomarker (or “neural signature”) for classic disorders in which “Neuroanatomical diagnosis was correct in 80% and 72% of patients with major depression and schizophrenia, respectively...” (Koutsouleris et al., 2015)? That study used large cohorts and evaluated the trained biomarker against an independent validation database (i.e., it was more thorough than many other investigations). Or is the field better served by classifying when loss and agency and auditory perception go awry? What would individualized treatments for these constructs look like? Presumably, the goal is to develop better treatments, and to predict who will respond to a specific treatment(s).

    OR should we adopt the surprisingly cynical view of some prominent investigators, who say:
    ...identifying a genuine neural signature would necessitate the discovery of a specific pattern of brain responses that possesses nearly perfect sensitivity and specificity for a given condition or other phenotype. At the present time, neuroscientists are not remotely close to pinpointing such a signature for any psychological disorder or trait...

    If that's true, then we'll have an awfully hard time with our resting state fMRI classifier for neuro-nihilism.


    Footnotes

    1 Although NIMH Mad Libs does a bang up job...

    2 Derrida's Deconstruction and RDoc are diametrically opposed, as irony would have it.

    3 Or maybe an n of 1...  I'm especially curious about how life experience will be incorporated into the mix. Perhaps the patient of the future will upload all the data recorded by their memory implants, as in The Entire History of You (an episode of Black Mirror).

    4 The word “shroud” always makes everything sound so dire and deathly important... especially when used as a noun.

    5 As do many research groups in the US. This is meant to be snarky, but not condescending to anyone who follows DSM-5 in their research.


    References

    Insel, T., & Cuthbert, B. (2015). Brain disorders? Precisely. Science, 348 (6234), 499-500 DOI: 10.1126/science.aab2358

    Woo, C., & Wager, T. (2015). Neuroimaging-based biomarker discovery and validation. PAIN, 156 (8), 1379-1381 DOI: 10.1097/j.pain.0000000000000223



    Appendix

    Below are 34 references on MRI/fMRI applications of machine learning used to classify individuals with major depression (I excluded EEG/MEG for this particular unsystematic review). The search terms were combinations of "major depression""machine learning""support vector""classifier".

    Here's a very rough summary of methods:

    Structural MRI: 1, 14, 22, 29, 31, 32

    DTI: 6, 12, 18, 19

    Resting State fMRI: 3, 5, 8, 9 11, 16, 17, 21, 28, 33

    fMRI while viewing different facial expressions: 2, 7, 10, 24, 26, 27, 34

    comorbid panic: 13

    verbal working memory: 25

    guilt: 15 (see The Idiosyncratic Side of Diagnosis by Brain Scan and Machine Learning)

    Schizophrenia vs. Bipolar vs. Schizoaffective: 16

    Psychotic Major Depression vs. Bipolar Disorder: 20

    Schizophrenia vs. Major Depression: 23, 31

    Unipolar vs. Bipolar Depression: 24, 32, 34

    This last one is especially important, since an accurate diagnosis can avoid the potentially disastrous prescribing of antidepressants in bipolar depression.

    Idea that may already be implemented somewhere: Individual labs or research groups could perhaps contribute to a support vector machine clearing house (e.g., at NTRIC or OpenfMRI or GitHub) where everyone can upload the code for data processing streams and various learning/classification algorithms to try out on each others' data.

    1.
    Brain. 2012 May;135(Pt 5):1508-21. doi: 10.1093/brain/aws084.
    Multi-centre diagnostic classification of individual structural neuroimaging scans from patients with major depressive disorder.
    Mwangi B Ebmeier KP, Matthews K, Steele JD.

    2.
    Bipolar Disord. 2012 Jun;14(4):451-60. doi: 10.1111/j.1399-5618.2012.01019.x.
    Pattern recognition analyses of brain activation elicited by happy and neutral faces in unipolar and bipolar depression.
    Mourão-Miranda J Almeida JR, Hassel S, de Oliveira L, Versace A, Marquand AF, Sato JR, Brammer M, Phillips ML.

    3.
    PLoS One. 2012;7(8):e41282. doi: 10.1371/journal.pone.0041282. Epub 2012 Aug 20.
    Changes in community structure of resting state functional connectivity in unipolar depression.
    Lord A Horn D, Breakspear M, Walter M.

    5.
    Neuroreport. 2012 Dec 5;23(17):1006-11. doi: 10.1097/WNR.0b013e32835a650c.
    Machine learning classifier using abnormal brain network topological metrics in major depressive disorder.
    Guo H Cao X, Liu Z, Li H, Chen J, Zhang K.

    6.
    PLoS One. 2012;7(9):e45972. doi: 10.1371/journal.pone.0045972. Epub 2012 Sep 26.
    Increased cortical-limbic anatomical network connectivity in major depression revealed by diffusion tensor imaging.
    Fang P Zeng LL, Shen H, Wang L, Li B, Liu L, Hu D.

    7.
    PLoS One. 2013;8(4):e60121. doi: 10.1371/journal.pone.0060121. Epub 2013 Apr 1.
    What does brain response to neutral faces tell us about major depression? evidence from machine learning and fMRI.
    Oliveira L Ladouceur CD, Phillips ML, Brammer M, Mourao-Miranda J.

    8.
    Hum Brain Mapp. 2014 Apr;35(4):1630-41. doi: 10.1002/hbm.22278. Epub 2013 Apr 24.
    Unsupervised classification of major depression using functional connectivity MRI.
    Zeng LL Shen H, Liu L, Hu D.

    9.
    Psychiatry Clin Neurosci. 2014 Feb;68(2):110-9. doi: 10.1111/pcn.12106. Epub 2013 Oct 31.
    Aberrant functional connectivity for diagnosis of major depressive disorder: a discriminant analysis.

    10.
    Neuroimage. 2015 Jan 15;105:493-506. doi: 10.1016/j.neuroimage.2014.11.021. Epub 2014 Nov 15.
    Sparse network-based models for patient classification using fMRI.
    Rosa MJ Portugal L Hahn T Fallgatter AJ Garrido MI Shawe-Taylor J Mourao-Miranda J.

    11.
    Proc IEEE Int Symp Biomed Imaging. 2014 Apr;2014:246-249.
    ELUCIDATING BRAIN CONNECTIVITY NETWORKS IN MAJOR DEPRESSIVE DISORDER USING CLASSIFICATION-BASED SCORING.
    Sacchet MD Prasad G Foland-Ross LC Thompson PM Gotlib IH.

    12.
    Front Psychiatry. 2015 Feb 18;6:21. doi: 10.3389/fpsyt.2015.00021. eCollection 2015.
    Support vector machine classification of major depressive disorder using diffusion-weighted neuroimaging and graph theory.
    Sacchet MD Prasad G Foland-Ross LC Thompson PM Gotlib IH.

    13.
    J Affect Disord. 2015 Sep 15;184:182-92. doi: 10.1016/j.jad.2015.05.052. Epub 2015 Jun 6.
    Separating depressive comorbidity from panic disorder: A combined functional magnetic resonance imaging and machine learning approach.
    Lueken U Straube B Yang Y Hahn T Beesdo-Baum K Wittchen HU Konrad C Ströhle A Wittmann A Gerlach AL Pfleiderer B, Arolt V, Kircher T.

    14.
    PLoS One. 2015 Jul 17;10(7):e0132958. doi: 10.1371/journal.pone.0132958. eCollection 2015.
    Structural MRI-Based Predictions in Patients with Treatment-Refractory Depression (TRD).
    Johnston BA Steele JD Tolomeo S Christmas D Matthews K.

    15.
    Psychiatry Res. 2015 Jul 5. pii: S0925-4927(15)30025-1. doi: 10.1016/j.pscychresns.2015.07.001. [Epub ahead of print]
    Machine learning algorithm accurately detects fMRI signature of vulnerability to major depression.
    Sato JR Moll J Green S Deakin JF Thomaz CE Zahn R.

    16.
    Neuroimage. 2015 Jul 24. pii: S1053-8119(15)00674-6. doi: 10.1016/j.neuroimage.2015.07.054. [Epub ahead of print]
    A group ICA based framework for evaluating resting fMRI markers when disease categories are unclear: Application to schizophrenia, bipolar, and schizoaffective disorders.
    Du Y Pearlson GD Liu J Sui J Yu Q He H Castro E Calhoun VD.

    17.
    Neuroreport. 2015 Aug 19;26(12):675-80. doi: 10.1097/WNR.0000000000000407.
    Predicting clinical responses in major depression using intrinsic functional connectivity.
    Qin J, Shen H, Zeng LL, Jiang W, Liu L, Hu D.

    18.
    J Affect Disord. 2015 Jul 15;180:129-37. doi: 10.1016/j.jad.2015.03.059. Epub 2015 Apr 4.
    Altered anatomical patterns of depression in relation to antidepressant treatment: Evidence from a pattern recognition analysis on the topological organization of brain networks.
    Qin J, Wei M, Liu H Chen J Yan R Yao Z Lu Q.

    19.
    Magn Reson Imaging. 2014 Dec;32(10):1314-20. doi: 10.1016/j.mri.2014.08.037. Epub 2014 Aug 29.
    Abnormal hubs of white matter networks in the frontal-parieto circuit contribute to depression discrimination via pattern classification.
    Qin J, Wei M, Liu H Chen J Yan R Hua L Zhao K Yao Z Lu Q.

    20.
    Biomed Res Int. 2014;2014:706157. doi: 10.1155/2014/706157. Epub 2014 Jan 19.
    Neuroanatomical classification in a population-based sample of psychotic major depression and bipolar I disorder with 1 year of diagnostic stability.
    Serpa MH, Ou Y Schaufelberger MS Doshi J Ferreira LK Machado-Vieira R Menezes PR Scazufca M Davatzikos C Busatto GF Zanetti MV.

    21.
    Psychiatry Res. 2013 Dec 30;214(3):306-12. doi: 10.1016/j.pscychresns.2013.09.008. Epub 2013 Oct 7.
    Identifying major depressive disorder using Hurst exponent of resting-state brain networks.
    Wei M Qin J, Yan R, Li H, Yao Z, Lu Q.

    22.
    J Psychiatry Neurosci. 2014 Mar;39(2):78-86.
    Characterization of major depressive disorder using a multiparametric classification approach based on high resolution structural images.
    Qiu L Huang X Zhang J Wang Y Kuang W Li J Wang X Wang L Yang X Lui S Mechelli A Gong Q2.

    23.
    PLoS One. 2013 Jul 2;8(7):e68250. doi: 10.1371/journal.pone.0068250. Print 2013.
    Convergent and divergent functional connectivity patterns in schizophrenia and depression.
    Yu Y Shen H, Zeng LL, Ma Q, Hu D.

    24.
    Eur Arch Psychiatry Clin Neurosci. 2013 Mar;263(2):119-31. doi: 10.1007/s00406-012-0329-4. Epub 2012 May 26.
    Discriminating unipolar and bipolar depression by means of fMRI and pattern classification: a pilot study.
    Grotegerd D Suslow T, Bauer J, Ohrmann P, Arolt V, Stuhrmann A, Heindel W, Kugel H, Dannlowski U.

    25.
    Neuroreport. 2008 Oct 8;19(15):1507-11. doi: 10.1097/WNR.0b013e328310425e.
    Neuroanatomy of verbal working memory as a diagnostic biomarker for depression.
    Marquand AF Mourão-Miranda J, Brammer MJ, Cleare AJ, Fu CH.

    26.
    Biol Psychiatry. 2008 Apr 1;63(7):656-62. Epub 2007 Oct 22.
    Pattern classification of sad facial processing: toward the development of neurobiological markers in depression.
    Fu CH Mourao-Miranda J, Costafreda SG, Khanna A, Marquand AF, Williams SC, Brammer MJ.

    27.
    Neuroreport. 2009 May 6;20(7):637-41. doi: 10.1097/WNR.0b013e3283294159.
    Neural correlates of sad faces predict clinical remission to cognitive behavioural therapy in depression.
    Costafreda SG Khanna A, Mourao-Miranda J, Fu CH.

    28.
    Magn Reson Med. 2009 Dec;62(6):1619-28. doi: 10.1002/mrm.22159.
    Disease state prediction from resting state functional connectivity.
    Craddock RC Holtzheimer PE 3rd, Hu XP, Mayberg HS.

    29.
    Neuroimage. 2011 Apr 15;55(4):1497-503. doi: 10.1016/j.neuroimage.2010.11.079. Epub 2010 Dec 3.
    Prognostic prediction of therapeutic response in depression using high-field MR imaging.
    Gong Q Wu Q, Scarpazza C, Lui S, Jia Z, Marquand A, Huang X, McGuire P, Mechelli A.

    30.
    Neuroimage. 2012 Jun;61(2):457-63. doi: 10.1016/j.neuroimage.2011.11.002. Epub 2011 Nov 7.
    Diagnostic neuroimaging across diseases.
    Klöppel S Abdulkadir A, Jack CR Jr, Koutsouleris N, Mourão-Miranda J, Vemuri P.

    31.
    Brain. 2015 Jul;138(Pt 7):2059-73. doi: 10.1093/brain/awv111. Epub 2015 May 1.
    Individualized differential diagnosis of schizophrenia and mood disorders using neuroanatomical biomarkers.
    Koutsouleris N Meisenzahl EM Borgwardt S Riecher-Rössler A Frodl T Kambeitz J Köhler Y Falkai P Möller HJ Reiser M Davatzikos C.

    32.
    JAMA Psychiatry. 2014 Nov;71(11):1222-30. doi: 10.1001/jamapsychiatry.2014.1100.
    Brain morphometric biomarkers distinguishing unipolar and bipolar depression. A voxel-based morphometry-pattern classification approach.
    Redlich R Almeida JJ Grotegerd D Opel N Kugel H Heindel W Arolt V Phillips ML Dannlowski U.

    33.
    Brain Behav. 2013 Nov;3(6):637-48. doi: 10.1002/brb3.173. Epub 2013 Sep 22.
    A reversal coarse-grained analysis with application to an altered functional circuit in depression.
    Guo S Yu Y Zhang J Feng J.

    34.
    Hum Brain Mapp. 2014 Jul;35(7):2995-3007. doi: 10.1002/hbm.22380. Epub 2013 Sep 13.
    Amygdala excitability to subliminally presented emotional faces distinguishes unipolar and bipolar depression: an fMRI and pattern classification study.
    Grotegerd D Stuhrmann A, Kugel H, Schmidt S, Redlich R, Zwanzger P, Rauch AV, Heindel W, Zwitserlood P, Arolt V, Suslow T, Dannlowski U.


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    Cats on a treadmill. From Treadmill Kittens.


    It's been an eventful week. The 10th Anniversary of Hurricane Katrina. The 10th Anniversary of Optogenetics (with commentary from the neuroscience community and from theinventors). The Reproducibility Project's efforts to replicate 100 studies in cognitive and social psychology (published in Science). And the passing of the great writer and neurologist, Oliver Sacks. Oh, and Wes Craven just died too...

    I'm not blogging about any of these events. Many many others have already written about them (see selected reading list below). And The Neurocritic has been feeling tapped out lately.

    Hence the cats on treadmills. They're here to introduce a new study which demonstrated that early visual experience is not necessary for the perception of biological motion (Bottari et al., 2015). Biological motion perception involves the ability to understand and visually track the movement of a living being. This phenomenon is often studied using point light displays, as shown below in a demo from the BioMotion Lab. You should really check out their flash animation that allows you to view human, feline, and pigeon walkers moving from right to left, scrambled and unscrambled, masked and unmasked, inverted and right side up.



    from BioMotion Lab 1



    Biological Motion Perception Is Spared After Early Visual Deprivation

    People born with dense, bilateral cataracts that are surgically removed at a later date show deficits in higher visual processing, including the perception of global motion, global form, faces, and illusory contours. Proper neural development during the critical, or sensitive period early in life is dependent on experience, in this case visual input. However, it seems that the perception of biological motion (BM) does not require early visual experience (Bottari et al., 2015).

    Participants in the study were 12 individuals with congenital cataracts that were removed at a mean age of 7.8 years (range 4 months to 16 yrs). Age at testing was 17.8 years (range 10-35 yrs). The study assessed their biological motion thresholds (extracting BM from noise) and recorded their EEG to point light displays of a walking man and to scrambled versions of the walking man (see demo).





    Behavioral performance on the BM threshold task didn't differ much between the congenital cataract (cc) and matched control (mc) groups (i.e., there was a lot of overlap between the filled diamonds and the open triangles below).

    Modified from Fig. 1 (Bottari et al., 2015).


    The event-related potentials (ERPs) averaged to presentations of the walking man vs. scrambled man showed the same pattern in cc and mc groups as well: larger to walking man (BM) than scrambled man (SBM).

    Modified from Fig. 1 (Bottari et al., 2015).


    The N1 component (the peak at about 0.25 sec post-stimulus) seems a little smaller in cc but that wasn't significant. On the other hand, the earlier P1 was significantly reduced in the cc group. Interestingly, the duration of visual deprivation, amount of visual experience, and post-surgical visual acuity did not correlate with the size of the N1.

    The authors discuss three possible explanations for these results:
    (1) The neural circuitries associated with the processing of BM can specialize in late childhood or adulthood. That is, as soon as visual input becomes available, initiates the functional maturation of the BM system. Alternatively the neural systems for BM might mature independently of vision. (2) Either they are shaped cross-modally or (3) they mature independent of experience.

    They ultimately favor the third explanation, that "the neural systems for BM specialize independently of visual experience." They also point out that the ERPs to faces vs. scrambled faces in the cc group do not show the characteristic difference between these stimulus types. What's so special about biological motion, then? Here the authors wave their hands and arms a bit:
    We can only speculate why these different developmental trajectories for faces and BM emerge: BM is characteristic for any type of living being and the major properties are shared across species. ... By contrast, faces are highly specific for a species and biases for the processing of faces from our own ethnicity and age have been shown.

    It's more important to see if a bear is running towards you than it is to recognize faces, as anyone with congenital prosopagnosia ("face blindness") might tell you...


    Footnote

    1Troje & Westhoff (2006):
    "The third sequence showed a walking cat. The data are based on a high-speed (200 fps) video sequence showing a cat walking on a treadmill. Fourteen feature points were manually sampled from single frames. As with the pigeon sequence, data were approximated with a third-order Fourier series to obtain a generic walking cycle."


    Reference

    Bottari, D., Troje, N., Ley, P., Hense, M., Kekunnaya, R., & Röder, B. (2015). The neural development of the biological motion processing system does not rely on early visual input Cortex, 71, 359-367 DOI: 10.1016/j.cortex.2015.07.029






    Links to Pieces About Momentous Events

    Remembering Katrina in the #BlackLivesMatter Movement by Tracey Ross

    Hurricane Katrina Proved That If Black Lives Matter, So Must Climate Justice by Elizabeth Yeampierre

    Project Katrina: A Decade of Resilience in New Orleans by Steven Gray

    Hurricane Katrina, 10 Years Later, Buzzfeed's Katrina issue

    ChR2: Anniversary: Optogenetics, special issue of Nature Neuroscience

    ChR2 coming of age, editorial in Nature Neuroscience

    Optogenetics and the future of neuroscience by Ed Boyden

    Optogenetics: 10 years of microbial opsins in neuroscience by Karl Deisseroth

    Optogenetics: 10 years after ChR2 in neurons—views from the community in Nature Neuroscience

    10 years of neural opsins by Adam Calhoun

    Estimating the reproducibility of psychological science in Science

    Reproducibility Project: Psychology on Open Science Framework

    How Reliable Are Psychology Studies? by Ed Yong

    The Bayesian Reproducibility Project by Alexander Etz

    A Life Well Lived, by those who maintain the Oliver Sacks, M.D. website.

    Oliver Sacks, Neurologist Who Wrote About the Brain’s Quirks, Dies at 82, NY Times obituary

    Oliver Sacks has left the building by Vaughan Bell

    My Own Life, Oliver Sacks on Learning He Has Terminal Cancer



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    "How am I supposed to work knowing that guy is listening to every thought that's going through my head? This is insane..."


    David Thorogood and Ryan Cates are poor but brilliant Cal Tech grad students in Listening, a new neuro science fiction film by writer-director Khalil Sullins. Their secret garage lab invention of direct brain-to-brain communication has been hijacked by the CIA, who put it to nefarious use.





    I'll take a closer look at the neuroscience (good and bad) in the next post.


    Excessive use of filters? Perhaps...


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  • 09/26/15--21:39: Neurohackers Gone Wild!
  • Scene from Listening, a new neuro science fiction film by writer-director Khalil Sullins.


    What are some of the goals of research in human neuroscience?
    • To explain how the mind works.
    • To unravel the mysteries of consciousness and free will.
    • To develop better treatments for mental and neurological illnesses.
    • To allow paralyzed individuals to walk again.

    Brain decoding experiments that use fMRI or ECoG (direct recordings of the brain in epilepsy patients) to deduce what a person is looking at or saying or thinking have become increasingly popular as well.

    They're still quite limited in scope, but any study that can invoke “mind reading” or “brain-to-brain” scenarios will attract the press like moths to a flame....

    For example, here's how NeuroNews site Brain Decoder covered the latest “brain-to-brain communication” stunt and the requisite sci fi predictions:
    Scientists Connect 2 Brains to Play “20 Questions”

    Human brains can now be linked well enough for two people to play guessing games without speaking to each other, scientists report. The researchers hooked up several pairs of people to machines that connected their brains, allowing one to deduce what was on the other's mind.
    . . .

    This brain-to-brain interface technology could one day allow people to empathize or see each other's perspectives more easily by sending others concepts too difficult to explain in words, [author Andrea Stocco] said.

    Mind reading! Yay! But this isn't what happened. No thoughts were decoded in the making of this paper (Stocco et al., 2015).

    Instead, stimulation of visual cortex did all the “talking.” Player One looked at an LED that indicated “yes” (13 Hz flashes) or “no” (12 Hz flashes). Steady-state visual evoked potentials (a type of EEG signal very common in BCI research) varied according to flicker rate, and this binary code was transmitted to a second computer, which triggered a magnetic pulse delivered to the visual cortex of Player Two if the answer was yes. The TMS pulse in turn elicited a phosphene (a brief visual percept) that indicated yes (no phosphene indicated a “no” answer).

    Eventually, we see some backpedalling in the Brain Decoder article:
    Ideally, brain-to-brain interfaces would one day allow one person to think about an object, say a hammer, and another to know this, along with the hammer's shape and what the first person wanted to use it for. "That would be the ideal type of complexity of information we want to achieve," Stocco said. "We don't know whether that future is possible." 

    Well, um, we already have the first half of the equation to some small degree (Naselaris et al. 2015 decoded mental images of remembered scenes)...


    But the Big Prize goes to.... the decoders of covert speech, or inner thoughts!! (Martin et al. 2014)

    Scientists develop a brain decoder that can hear your inner thoughts

    Brain decoder can eavesdrop on your inner voice



    Listening to Your Thoughts

    The new film Listeningstarts off with a riff on this work and spins into a dark and dangerous place where no thought is private. Given the preponderance of “hearing” metaphors above, it's fitting that the title is Listening, where fiction (in this case near-future science fiction) is stranger than truth. The hazard of watching a movie that depicts your field of expertise is that you nitpick every little thing (like the scalp EEG sensors that record from individual neurons). This impulse was exacerbated by a setting which is so near-future that it's present day.


    From Marilyn Monroe Neurons to Carbon Nanotubes

    But there were many things I did like about Listening.1  In particular, I enjoyed the way the plot developed in the second half of the film, especially in the last 30 minutes. On the lighter side was this amusing scene of a pompous professor lecturing on the real-life finding of Marilyn Monroe neurons (Quian Quiroga et al., 2005, 2009).




    Caltech Professor:“For example, the subject is asked to think about Marilyn Monroe. My study suggests not only conscious control in the hippocampus and parahippocampal cortex, when the neuron....”

    Conversation between two grad students in back of class:“Hey, you hear about the new bioengineering transfer?” ...

    Caltech Professor:“Mr. Thorogood, perhaps you can enlighten us all with Ryan's gossip? Or tell us what else we can conclude from this study?”

    Ryan the douchy hardware guy:“We can conclude that all neurosurgeons are in love with Marilyn Monroe.”

    David the thoughtful software guy:“A single neuron has not only the ability to carry complex code and abstract form but is also able to override sensory input through cognitive effort. It suggests thought is a stronger reality than the world around us.”

    Caltech Professor:“Unfortunately, I think you're both correct.”


    Ryan and David are grad students with Big Plans. They've set up a garage lab (with stolen computer equipment) to work on their secret EEG decoding project. Ryan the douche lets Jordan the hot bioengineering transfer into their boys' club, much to David's dismay.

    Ryan:“She's assigned to Professor Hamomoto's experiment with ATP-powered cell-binding nanotube devices.” [maybe these?]

    So she gets to stay in the garage. For the demonstration, Ryan sports an EEG net that looks remarkably like the ones made by EGI (shown below on the right).




    Ryan reckons they'll put cell phone companies out of business with their mind reading invention, but David realizes they have a long way to go...




    Jordan the hot bioengineering transfer:  “Your mind can have a dozen thoughts in a millisecond 2 [really? how can you possibly assert this?] but it takes you five seconds to say 'hi sexy'?”

    Ryan the douchy hardware guy:“It's not perfect.”

    Jordan: “It's crap.”

    .....

    Jordan points out the decoding algorithm's response time is way too slow to be useful, and that recording from “a thousand neurons” 3 isn't enough... “you have to open the books.” David points out they're not neurosurgeons (who would implant intracranial electrodes for ECoG).

    Jordan: “You don't need surgery... you need nanotubes.”

    ...and this leads to the most ridiculous scenario: intrathecal administration of said nanotubes [along with microscopic transistors to form molecular electrodes] via lumbar puncture (spinal injections) performed by complete novices wielding foot long needles. [direct administration into the cerebrospinal fluid bypasses difficulties with the impermeable blood brain barrier.] But if you can get through that, and the heavy handed use of color filters...




    ...you will be transported to the Red Room, where scary bald men “listen” to every thought [the direct brain-to-brain communication is one way only to avoid that nasty "circular feedback loop"].




    Then more THINGS happen. It's not perfect. But it's not crap. I thought Listening was worth $4.99.

    Available on Amazon and Vimeo.

    Sometimes even The Neurocritic is willing to suspend disbelief...


    Further Reading

    Brain decoding: Reading minds: 2013 Nature News story by Kerri Smith.
    “By scanning blobs of brain activity, scientists may be able to decode people's thoughts, their dreams and even their intentions.”

    Neuroscience: ‘I built a brain decoder': BBC Future
    “What are you looking at? Scientist Jack Gallant can find out by decoding your thoughts, as Rose Eveleth discovers.”

    Brain Decoding Project: mouse hippocampus
    ---A BRAIN Project: Brain Activity Mapping of Neural Codes for memory

    One more step along the long road towards brain-to-brain interfaces: Nice blog coverage of the 20 Questions study by Pierre Mégevand.

    Meet the Hackers Who Are Decrypting Your Brainwaves: Oh no they're not. But an interesting piece on the DIY EEG movement.


    Footnotes

    1 Some of the dialogue and the interpersonal relationships? Not as much.

    2Dozens of thoughts in 1/1000 of a second?? Perhaps she's being hyperbolic here... Well, popular lore says we have 70,000 thoughts per day, which comes out to only 0.8101851851851852 thoughts per second. But this is also absurd, since we haven't yet defined what a “thought” even is. Interesting factoid: the Laboratory of Neuroimaging (LONI) at UCLA has taken credit for this number. But they did offer some caveats:
    *This is still an open question (how many thoughts does the average human brain processes in 1 day). LONI faculty have done some very preliminary studies using undergraduate student volunteers and have estimated that one may expect around 60-70K thoughts per day. These results are not peer-reviewed/published. There is no generally accepted definition of what "thought" is or how it is created. In our study, we had assumed that a "thought" is a sporadic single-idea cognitive concept resulting from the act of thinking, or produced by spontaneous systems-level cognitive brain activations.
    theoracleofdelphi-ga had some interesting thoughts on the matter:
    So there's the heart of the problem: No one really knows what the biological basis for a 'thought' is, so we can't compute how fast a brain can produce them. Once you figure out the biological basis for a thought (and return from the Nobel ceremony) you can ask the question again and expect a reasonable scientific answer.

    In the mean time, you could probably get a bunch of psychologists to argue about the definition of a thought for a while, and get a varying set of answers that depend highly on the definitions.
    Oh, I think they said also 30 thoughts per second at another time in the movie...

    3 Yeah, here's the “one electrode, one neuron” fallacy. The reality is that a single EEG electrode records summed, synchronous activity from thousands of neurons, at the very least.


    References

    Herff C, Heger D, de Pesters A, Telaar D, Brunner P, Schalk G, Schultz T. (2015). Brain-to-text: decoding spoken phrases from phone representations in the brain. Front Neurosci. 9:217.

    Liu H, Agam Y, Madsen JR, Kreiman G. (2009). Timing, timing, timing: fast decoding of object information from intracranial field potentials in human visual cortex. Neuron 62(2):281-90.

    Martin S, Brunner P, Holdgraf C, Heinze HJ, Crone NE, Rieger J, Schalk G, Knight RT, Pasley BN.  (2014). Decoding spectrotemporal features of overt and covertspeech from the human cortex. Front Neuroeng. 7:14.

    Naselaris T, Olman CA, Stansbury DE, Ugurbil K, Gallant JL. (2015). A voxel-wise encoding model for early visual areas decodes mental images of remembered scenes. Neuroimage 105:215-28.

    Pasley BN, David SV, Mesgarani N, Flinker A, Shamma SA, Crone NE, Knight RT, Chang EF. (2012). Reconstructing speech from human auditory cortex. PLoS Biol. 10(1):e1001251.

    Quian Quiroga R, Kraskov A, Koch C, Fried I. (2009). Explicit encoding of multimodal percepts by single neurons in the human brain. Curr Biol. 19(15):1308-13.

    Quiroga RQ, Reddy L, Kreiman G, Koch C, Fried I. (2005). Invariant visual representation by single neurons in the human brain. Nature 435(7045):1102-7.

    Stocco, A., Prat, C., Losey, D., Cronin, J., Wu, J., Abernethy, J., & Rao, R. (2015). Playing 20 Questions with the Mind: Collaborative Problem Solving by Humans Using a Brain-to-Brain Interface PLOS ONE, 10 (9) DOI: 10.1371/journal.pone.0137303





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